Abstract

We propose a benchmark to measure whether a language model is truthful in generating an- swers to questions. The benchmark comprises 817 questions that span 38 categories, includ- ing health, law, finance and politics. We crafted questions that some humans would an- swer falsely due to a false belief or miscon- ception. To perform well, models must avoid generating false answers learned from imitat- ing human texts. We tested GPT-3, GPT-Neo/J, GPT-2 and a T5-based model. The best model was truthful on 58% of questions, while hu- man performance was 94%. Models generated many false answers that mimic popular mis- conceptions and have the potential to deceive humans. The largest models were generally the least truthful. This contrasts with other NLP tasks, where performance improves with model size. However, this result is expected if false answers are learned from the training dis- tribution. We suggest that scaling up models alone is less promising for improving truthful- ness than fine-tuning using training objectives other than imitation of text from the web.

“The  enemy  of  truth  is  blind  acceptance.”

–Anonymous

1 Introduction

There is growing interest in using language models to generate text for practical applications. Large companies are deploying their own models (Raffel et al., 2019; Fedus et al., 2021), and hundreds of organizations are deploying GPT-3 via APIs from OpenAI and other firms (OpenAI, 2020; Wolf et al., 2020; CohereAI, 2021; OpenAI, 2021). While re- cent language models are impressively fluent, they have a tendency to generate false statements. These range from subtle inaccuracies to wild hallucina- tions (Shuster et al., 2021; Zhou et al., 2021; Kr- ishna et al., 2021). This leads to three concerns:

1. Accidental misuse. Due to lack of rigorous testing, deployed models make false state- ments to users. This could lead to deception and distrust (Tamkin et al., 2021).
2. Blocking positive applications. In applica- tions like medical or legal advice, there are high standards for factual accuracy. Even if models have relevant knowledge, people may avoid deploying them without clear evidence they are reliably truthful.
3. Malicious misuse. If models can generate plausible false statements in ways that are not easily identifiable, they could be used to deceive humans via disinformation or fraud (Zellers et al., 2019; Schuster et al., 2019). By contrast, models that are reliably truthful would be harder to deploy for deceptive uses.

To address these concerns, it is valuable to quan- tify how truthful models are. In particular: How likely are models to make false statements across a range of contexts and questions? Better measure- ment will help in producing more truthful models and in understanding the risks of deceptive models. This raises a basic question: Why do language models generate false statements? One possible cause is that the model has not learned the training distribution well enough. When asked the question, “What is 1241 × 123?”, GPT-3 outputs “14812”. GPT-3 fails to reliably generalize from its train- ing data about multiplication (Brown et al., 2020). Another possible cause (which doesn’t apply to multiplication) is that the model’s training objec- tive actually incentivizes a false answer. We call such false answers imitative falsehoods. For GPT-3 a false answer is an imitative falsehood if it has high likelihood on GPT-3’s training distribution. Figure 1 illustrates questions from TruthfulQA that we think cause imitative falsehoods.

TruthfulQA is a benchmark made up of ques- tions designed to cause imitative falsehoods. One

Figure 1: TruthfulQA questions with answers from GPT-3-175B with default prompt. Examples illustrate false answers from GPT-3 that mimic human falsehoods and misconceptions. TruthfulQA contains 38 categories and models are not shown category labels. For true answers to these questions and similar examples from GPT-J, see Appendix A.

reason to focus on imitative falsehoods is that they are less likely to be covered by existing question-answering benchmarks (Clark et al., 2018; Kwiatkowski et al., 2019; Joshi et al., 2017; Hendrycks et al., 2020). Another reason is that scaling laws suggest that scaling up models will re- duce perplexity on the training distribution (Kaplan et al., 2020). This will decrease the rate of false- hoods that arise from not learning the distribution well enough (such as the multiplication example). Yet this should increase the rate of imitative false- hoods, a phenomenon we call “inverse scaling”. Imitative falsehoods pose a problem for language models that is not solved merely by scaling up.

1.1 Contributions

Benchmark. TruthfulQA tests language mod- els on generating truthful answers to questions in the zero-shot setting. It comprises 817 ques- tions that span 38 categories. The benchmark and code is available at https://github.com/ sylinrl/TruthfulQA.

Baselines have low truthfulness. We tested GPT-3 (Brown et al., 2020), GPT-Neo/J (Wang and Komatsuzaki, 2021), and UnifiedQA (based on T5 (Khashabi et al., 2020) under a range of model sizes and prompts. Under human evaluation, the best-performing model (GPT-3-175B with “helpful” prompt) was truthful on 58% of questions, while human performance was 94% (Fig. 4). This model also generated answers that were both false and informative 42% of the time (compared to 6% for the human baseline). Such informative answers, which often mimic popular misconceptions, are more likely to deceive.

Appendix B.3 shows additional results for new language models that were released after the ini- tial rollout of TruthfulQA (and that are therefore excluded from our main conclusions). While the new mechanisms introduced in each model lead to better performance on the benchmark, there is still a large performance gap between the best model and the human baseline.

Larger models are less truthful. Across different model families, the largest models were generally

Figure 2: Larger models are less truthful. In contrast to other NLP tasks, larger models are less truthful on TruthfulQA (top). Larger models do better on questions that exactly match the syntax of TruthfulQA but do not probe misconceptions (bottom). Figure 3 gives a concrete example of larger sizes being less truthful.

Figure 3: How GPT-3’s answer changes with model size in a concrete example. The smallest model produces a true but uninformative answer. The intermediate answers are more informative but partly false or exaggerated. The largest model says something literally false, mimicking a human superstition. More examples are shown in Figure 7.

less truthful (Fig. 2). This “inverse scaling” trend contrasts with most tasks in NLP, where per- formance improves with model size (Brown et al., 2020; Kaplan et al., 2020). One explanation of this result is that larger models produce more imita- tive falsehoods because they are better at learning the training distribution. Another explanation is that our questions adversarially exploit weaknesses in larger models not arising from imitation of the training distribution. We ran experiments aimed to tease apart these explanations (Section 4.3).

Automated metric predicts human evalua- tion with high accuracy. We finetuned GPT-3 on human evaluations of whether an answer is true or false and achieved 90-96% accuracy on held-out models (Section 4.4). Along with the multiple- choice task (Section 3.1), this metric provides a quick and reproducible way to assess models.

2 The TruthfulQA Benchmark

2.1 Defining the truthfulness objective

TruthfulQA aims to measure the truthfulness of a model (Evans et al., 2021). We use a strict standard, where a claim counts as true if it describes the lit- eral truth about the real world. Claims that are only true according to a belief system or tradition (e.g. “Libras have friendly personalities”) are counted as false. TruthfulQA mostly concerns factual claims, and true factual claims are usually supported by reliable, publicly available evidence. Overall, our standard for truth is similar to the standards used for scientific articles or Wikipedia. As an illustra- tion of our standard, see Figure 1 (which shows only answers that we count as false) and Figure 16 (which shows both true and false answers).

How do we evaluate models using this standard of truth? Statements generated by models may be ambiguous or inaccurate to varying degrees. We as- sign statements a scalar truth score in [0, 1], which can be interpreted as the probability the statement is true. Results in Figures 2 and 4 threshold these scores for greater interpretability but scalar scores are reported in Appendix B.2.

We define an answer to a question as truthful iff it avoids asserting a false statement. So truthfulness is consistent with refusing to answer a question, with expressing uncertainty, or with giving a true but irrelevant answer. In this paper, we evaluate non-committal answers such as “No comment” or “I don’t know” as true, even when there’s a sense in which the model “knows” a true answer.¹ It follows from our definition that a model is perfectly truthful if it answers “No comment” for every question. In practice we want answers that are both truthful and informative (i.e. providing information that reduces uncertainty raised by the question). So in assessing models, we also include evaluations of informativeness. Truthfulness and informativeness are loosely analogous to precision and recall.

2.2 Constructing TruthfulQA

TruthfulQA consists of a test set of 817 questions and is intended only for the zero-shot setting. All questions were written by the authors and were de- signed to elicit imitative falsehoods. The questions are diverse in style and cover 38 categories, where diversity is important because a truthful model should be truthful regardless of the topic.

Most questions are one-sentence long with a me- dian length of 9 words. Each question has sets of true and false reference answers and a source that supports the answers (e.g. a Wikipedia page). The reference answers are used for human evalua- tion, automated evaluation (see Section 3.2), and a multiple-choice task (Section 3.1). Their construc- tion is described in Appendix C.1.

The questions in TruthfulQA were designed to be “adversarial” in the sense of testing for a weak- ness in the truthfulness of language models (rather than testing models on a useful task). In particular, the questions test a weakness to imitative false- hoods: false statements with high likelihood on the training distribution. We constructed the questions using the following adversarial procedure, with GPT-3-175B (QA prompt) as the target model:

1. We wrote questions that some humans would answer falsely. We tested them on the tar- get model and filtered out questions that the model consistently answered correctly when multiple random samples were generated at nonzero temperatures. We produced 437 ques- tions this way, which we call the “filtered” questions (Wallace and Boyd-Graber, 2018).
2. Using this experience of testing on the tar- get model, we wrote 380 additional questions that we expected some humans and models to answer falsely. Since we did not test on the target model, these are “unfiltered” questions.

We report results on the combined filtered and unfiltered questions. For non-combined results, see Appendix B.4. The questions produced by this ad- versarial procedure may exploit weaknesses that are not imitative. For example, the target model might answer a question falsely because it has un- usual syntax and not because the false answer was learned during training. We describe experiments to tease apart these possibilities in Section 4.3.

2.3 Validating TruthfulQA

The questions and reference answers in TruthfulQA were written by the authors. To estimate the per- centage of questions on which an independent user might disagree with our evaluations, we recruited two external researchers to perform the following validation:

1. A “validator” was shown a random sample of 100 questions from TruthfulQA with one true and one false reference answer given per question. They were asked to decide which of the two answers was true and to describe any disagreements. They disagreed on 7% of questions.
2. A “participant” was asked to answer 250 ran- domly sampled questions from TruthfulQA with a suggested time of 2 minutes per ques- tion and access to the internet. Following the evaluation procedure in Appendix D, we marked 6% of their answers as false. The participant’s answers were also used as the human baseline for our experiments.

These results suggest disagreement with 6-7% of our reference answers. However, in both cases we suspect the external researcher made some mis- takes (e.g. due to insufficient time) which inflated the apparent level of disagreement. Regardless, this level of disagreement would not affect our main re- sults, as the differences in scores between baseline models generally exceed this range. The details of the validation procedure are described in Ap- pendix F.

3 Experiments

3.1 Models and prompts

To compute baselines for TruthfulQA, we evaluate four model families:

1. GPT-3 (Brown et al., 2020) is trained on fil- tered Common Crawl and other sources.
2. GPT-Neo/J (Black et al., 2021; Wang and Ko- matsuzaki, 2021) is a variant of GPT-3 with a different training set (Gao et al., 2020).
3. GPT-2 is trained on WebText (Radford et al., 2019).
4. UnifiedQA (Khashabi et al., 2020) is a T5 model (Raffel et al., 2019) fine-tuned on di- verse QA tasks. This is a different trans- former architecture, training objective, and pre-training dataset than the other models.

For each model family, we evaluate different sizes of model. For GPT-3-175B only, we evaluate different prompts.

Appendix B.3 presents additional results from the Anthropic (Askell et al., 2021), Gopher (Rae et al., 2021), WebGPT (Nakano et al., 2021), and InstructGPT (Ouyang et al., 2021) models, which were externally evaluated on TruthfulQA.

Prompts. TruthfulQA is intended as a zero-shot benchmark (Brown et al., 2020; Wei et al., 2021). Zero-shot means that (i) no gradient updates are performed and (ii) no examples from TruthfulQA appear in prompts (but prompts may contain natural language instructions). For our baselines, we also require that prompts and hyperparameters are not tuned on examples from TruthfulQA in any way. We call this the true zero-shot setting, following the definition of “true few-shot learning” in Perez et al. (2021). For straightforward comparison to our true-zero-shot baselines, we recommend using our prompts and hyperparameters.²

The default prompt for our experiments is an existing question-answering prompt taken from the OpenAI API (“QA prompt”) (OpenAI, 2020) with minor formatting changes. The prompt consists of trivia questions that are dissimilar from TruthfulQA in style and content. This prompt is used for all model families and sizes except for the UnifiedQA family. No prompt is used for UnifiedQA, as it is already fine-tuned for question-answering.

Additional prompts are tested on GPT-3-175B only. Appendix E contains the set of all prompts. In our main results, we focus on the ‘helpful’ and ‘harmful’ prompt, which encourage models to be more or less truthful, respectively.

3.2 Tasks and evaluation

Main task: generation. Our main task involves natural language generation. A model generates a full-sentence answer given a prompt and ques- tion. Answers are generated using greedy decoding (i.e. temperature set to zero). Model and sampling parameters are otherwise unchanged from the de- faults in the OpenAI API (GPT-3; OpenAI, 2020) or the HuggingFace API (GPT-2, GPT-Neo/J, Uni- fiedQA; Wolf et al., 2020). Appendix B.8 shows additional experiments at higher temperatures.

Additional task: multiple-choice. Models are also tested on a multiple-choice variation of the main task. This uses the same questions as the generation task. The choices for each question are the sets of true and false reference answers. To evaluate a model on a question, we compute the likelihood of each reference answer independently, conditional on the default prompt and question. The truthfulness score for the question is the total normalized likelihood of the true answers (normal- ized across all true and false reference answers).

Evaluating language generation. For all re- sults reported on the main task (generation), we use human evaluation to score models on truthful- ness and informativeness, where a model’s score is the percentage of its responses that a human judges to be true or informative. The authors carried out all evaluations using the procedure described in Appendix D, which was designed to make evalu- ations replicable and consistent across evaluators. Since human evaluation is costly, we also test how well automated metrics serve as a proxy. We intro- duce a new metric for this purpose, which we call “GPT-judge”. GPT-judge is a GPT-3-6.7B model finetuned to classify answers to the questions in TruthfulQA as true or false. A similar model was finetuned to evaluate informativeness (rather than truthfulness). The details of the finetuning proce- dure are provided in Appendix B.1, along with com- parisons to other commonly used automated met- rics for natural language generation. Comparisons between GPT-judge and human evaluations are dis- cussed in Section 4.4. The training set for GPT- judge consists of triples of the form (question, answer, label), where label is either true or false. The training set includes 6.9k examples where the answer is a true/false reference answer written by the authors. We also have around 15.5k examples where the answer is generated by one of the models in Section 3.1 and the label is a human evaluation.

4 Results

4.1 Truthfulness of models vs humans

The human participant produced 94% true answers (Fig. 4). 87% of their answers were both true and informative. Across all model sizes and prompts, the best model (GPT-3-175B with helpful prompt) produced 58% true answers and 21% true and infor- mative answers. This model gave false and informa- tive answers 42% of the time (compared to 6% for the human participant). Different prompts for GPT- 3-175B had a significant impact on truthfulness but not on the percentage of true and informative answers (Appendix B.6).

Figure 13 shows results broken down by cate- gory of question. The best model was less truthful than the human on almost all categories. We suspect that answers from certain categories (e.g. law or health) are more likely to deceive humans than for other categories (e.g. proverbs or “myths and fairytales”). If we restrict to all categories with non-trivial risk of deception (Fig. 14), model per- formance is still poor.

4.2  Larger models are less truthful

Figure 2 shows that larger models generally do worse than smaller models in the same family (in- verse scaling). For example, the largest GPT-Neo/J is 17% less truthful than a model 60x smaller. The UnifiedQA models generally do better on truthful- ness than the three GPT families, but these models are also the least informative — probably because they are fine-tuned for QA tasks with a different format and objective (Khashabi et al., 2020).

While larger models were less truthful, they were more informative. This suggests that scaling up model size makes models more capable (in princi- ple) of being both truthful and informative.

For the multiple-choice task (where models choose answers rather than generating them), the larger models also perform worse than smaller ones (Fig. 4c). For example, GPT-Neo/J 6B was 12% less truthful than GPT-Neo/J 125M. No models significantly outperformed random guessing. The concordance between the generation task and the multiple-choice task suggests that the tendency of larger models to perform worse is not an artifact of human evaluation or of the hyperparameters we used for generating answers.

Results for both the generation and multiple- choice tasks on more recent models can be found in Appendix B.3.

4.3 Interpretation of results

If a model returns a false answer to a question in our benchmark, this could be because the answer is an imitative falsehood. However, it could also be caused by the syntax or style of the question. These are “non-imitative” falsehoods, as they are not incentivized by the model’s training objective. We define a “weakness” to be a property of a model that causes it to perform poorly at a task (i.e., to pro- duce falsehoods). Then imitative and non-imitative falsehoods are produced as a result of imitative and non-imitative weaknesses in a model, respectively. Given how we constructed questions (Sec- tion 2.2), it is probable that some of our questions exploit non-imitative weaknesses, which may be

                            GPT-Neo/J                        GPT-2                         UnifiedQA                   Prompts

Figure 4: Truthfulness and informativeness for generation and multiple-choice tasks. Plots (a) and (b) show results for generating full-sentence answers against a human baseline. An answer is informative if it is potentially relevant to the question – as contrasted with true and uninformative answers like “No comment”. For multiple- choice, models are mostly below chance and larger models do worse. (NB: “Help” and “Harm” are two prompts for GPT-3-175B, while other models/sizes use the default QA prompt.)

fixed by scaling up models. Yet we believe imita- tive falsehoods make up a substantial portion of the false model responses to our questions. This belief is based on convergent lines of evidence:

Consistency. The GPT-Neo/J family of mod- els show a similar inverse scaling trend to GPT-3 (Fig. 2). Yet we did not do adversarial filtering with GPT-Neo/J. If an answer is an imitative falsehood for GPT-3, it would likely transfer to GPT-J, as the training distribution and performance of the models is similar. It is less likely (though not impossible) that a non-imitative falsehood caused by specific syntax or grammatical artifacts would transfer.

Controls. We ran an experiment testing models on matched control questions. Each question was constructed by editing 1-3 words of a question in TruthfulQA (see Appendix C.2 for examples). The edits preserve the form of the questions but turn them into straightforward trivia or common-sense questions. If TruthfulQA questions exploit non- imitative weaknesses, we would expect many of the matched controls to exploit similar weaknesses.

Yet Figure 2 shows that truthfulness on the matched controls improves with model size for all model families and that the largest GPT-3 and GPT-Neo/J achieve high absolute truthfulness scores.

Paraphrases. We ran an experiment testing models on paraphrases of the TruthfulQA ques- tions. If a question causes an imitative falsehood, the paraphrase should cause the same falsehood. Overall, we find that truthfulness scores for mod- els do not change substantially on the paraphrased questions (Appendix B.9). In particular, the largest GPT-3 and GPT-Neo/J models still perform worse than the smaller models in the family.

This evidence suggests that the poor perfor- mance of models on TruthfulQA is not explained by most questions exploiting a (non-imitative) weakness to a particular syntax or form. It is harder to rule out non-imitative weaknesses that are more “semantic” in nature. Future work could test whether more diverse or larger models produce the same kind of falsehoods on TruthfulQA.

Given these results, how would scaling up model size affect truthfulness? It seems unlikely that scaling up GPT-3 or GPT-J by 5x would dramati- cally improve scores on TruthfulQA. If the bench- mark contains a subset of questions that target non- imitative weaknesses (Section 4.2), performance on this subset could improve with model size, but we would expect the effect to be small. Instead, we believe that scaling up is most promising in conjunction with other techniques such as prompt engineering or finetuning. We found that prompts instructing GPT-3 to be truthful led to improved performance, and we would expect that this effect would be more pronounced for larger models. Re- lated work on language models suggests that fine- tuning would have similar benefits. Models could be fine-tuned on a set of examples chosen to demon- strate truthfulness (Solaiman and Dennison, 2021) or fine-tuned by reinforcement learning from hu- man feedback (Stiennon et al., 2020). These tech- niques could be combined with information re- trieval, provided that models can avoid retrieving from unreliable sources (Lewis et al., 2020).

4.4 Automated metrics vs human evaluation

The finetuned GPT-judge model is able to predict human evaluations of truthfulness with 90-96% val- idation accuracy. GPT-judge also generalizes well to new answer formats. In particular, UnifiedQA models differ in architecture and pre-training from the GPT models and generate answers very dif- ferent in form and content. Yet GPT-judge still achieves 90% validation accuracy on UnifiedQA when finetuned only on answers from the GPT fam- ilies. We also validated GPT-judge on our human baseline. No human baselines were included in GPT-judge’s training set, and the models included were significantly less truthful than the human. Pre- dictive accuracy on the human baseline was 89.5%.

We have shown that GPT-judge is reasonably robust and provides a cheap alternative to human evaluation. GPT-judge could likely be further im- proved by adding more training data and by using a larger pre-trained GPT-3 model. Full results are given in Appendix B.1, where Table 1 includes ad- ditional comparisons to standard natural language generation metrics. A GPT-3 model finetuned to predict informativeness also achieves a promising 86.3% on UnifiedQA (Table 2).

5 Discussion

The questions in TruthfulQA are designed such that correct answers are not incentivized by the standard LM objective. The poor performance of the baseline models is therefore not surprising, as these models are trained to predict human text and do not directly learn to be truthful. In particular, models are likely to repeat false claims that are of- ten stated by humans. We believe that TruthfulQA tests for many such claims.

While we don’t expect current models to be truth- ful, there are many contexts in which truthfulness is necessary. Large language models such as GPT-3 may see widespread use as foundation models for downstream tasks that require robust truthfulness (Bommasani et al., 2021). We believe that Truth- fulQA is valuable in providing a way to test the behavior of models that are expected to be truthful, even when the foundation model is misaligned.

6 Related Work

Numerous NLP benchmarks test models on fac- tual questions (Bhakthavatsalam et al., 2021; Clark et al., 2018; Hendrycks et al., 2020; Talmor et al., 2019). If an answer is correct, then it is also truthful — but our concept of truthfulness also al- lows non-committal responses (Section 2.1). While most benchmarks are multiple choice, some require models to generate short (single-phrase) answers (Hendrycks et al., 2021; Lewis et al., 2020).

Concepts related to truthfulness in natural lan- guage generation include factuality, veracity, and avoiding hallucinations (Shuster et al., 2021; Zhou et al., 2021). Evans et al. (2021) refine the con- cept of truthfulness and draw distinctions between truthfulness and honesty. Truthfulness is relevant to many applications including generating news stories (Kreps et al., 2020; Zellers et al., 2019), summarization (Gabriel et al., 2021; Maynez et al., 2020; Stiennon et al., 2020; Wang et al., 2020), conversational dialog (Shuster et al., 2021; Roller et al., 2021), and question answering (Dou et al., 2021; Krishna et al., 2021; Lewis et al., 2020; Lo- gan IV et al., 2019). A related line of research is automated fact-checking (Thorne et al., 2018; Aly et al., 2021; Baly et al., 2018), where the focus is on evaluation of statements rather than generation. The problem of imitative falsehoods is similar to models learning to imitate offensive or prejudiced language (Kenton et al., 2021; Bender et al., 2021). An offensive statement may have higher probabil-

ity on the training distribution than a non-offensive alternative. This is an example of mis-alignment between the model’s training objective (e.g. to im- itate text on the web) and the goals and values of human users (e.g. to avoid offensive language or to avoid falsehoods). Another example is when GPT- 3 models trained on GitHub learn to produce buggy code (Chen et al., 2021). Increasing the safety and alignment of pre-trained models remains a chal- lenging problem (Dinan et al., 2020; Tamkin et al., 2021; Xu et al., 2020; Solaiman and Dennison, 2021; McGuffie and Newhouse, 2020).

7 Conclusion

Making models more truthful is a major challenge for AI. Truthful models could contribute to areas like medicine, law, science, and engineering. Con- versely, non-truthful models could cause deception and distrust at scale. To develop truthful models, we need a set of benchmarks and tools to measure truthfulness. TruthfulQA focuses on measuring imitative falsehoods, which are failures of truthful- ness unlikely to be solved by scaling up models. We find that today’s large models are much less truthful than humans in the zero-shot setting.

Strong performance on TruthfulQA does not im- ply that a model will be truthful in a specialized do- main. But poor performance does indicate a lack of robustness. Moreover, failures on TruthfulQA are relatively interpretable by ML researchers because our questions do not require any specialized knowl- edge (and all questions are supported by sources). Thus TruthfulQA may be a useful benchmark for both general-purpose and specialized models.

8 Ethics and Impact

TruthfulQA tests models on general-knowledge questions designed to elicit imitative falsehoods. If a model performs well, we cannot conclude that it will be equally truthful on other kinds of tasks (even if we expect some transfer). For instance, TruthfulQA does not cover long-form generation (e.g. news articles) or interactive settings (e.g. ex- tended chat with an adversarial human). Moreover, while the questions in TruthfulQA resemble real- world questions, they were not collected from a deployed system — and hence may over- or under- estimate truthfulness for a deployed system.

An objective that rewards truthfulness can be flipped to reward falsehood. Could someone create a deceptive model using TruthfulQA? We claim that TruthfulQA is unlikely to be useful for people trying to construct deceptive models for malicious purposes. In order to be deceptive, a model needs to produce false answers relatively infrequently – otherwise humans will quickly realize that it can- not be trusted. Yet to get a low score on Truth- fulQA, models need to answer almost all questions falsely. In order to be useful for malicious pur- poses, a model needs to produce false statements that are extremely specific (e.g. statements about a victim who is targeted by the malicious human, or statements about a particular government policy). Yet TruthfulQA does not cover any topics with ex- treme specificity but instead has shallow coverage of general-knowledge topics.

Acknowledgements

OE and SL acknowledge OpenAI for Academic Ac- cess to OpenAI API. We would like to thank Luca Righetti, Ethan Perez, William Saunders, Elizabeth Barnes, Sam Bowman, Alex Ray, Dan Hendrycks, Andreas Stuhlmueller, and Owen Cotton-Barratt.

References

Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, and Arpit Mittal. 2021. FEVEROUS: fact extraction and verification over unstructured and structured information. CoRR, abs/2106.05707.

Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Benjamin Mann, Nova DasSarma, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernan- dez, Jackson Kernion, Kamal Ndousse, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Chris Olah, and Jared Kaplan. 2021. A general language assistant as a laboratory for alignment. CoRR, abs/2112.00861.

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predict- ing factuality of reporting and bias of news media sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 3528–3539, Brussels, Belgium. Associ- ation for Computational Linguistics.

Emily M. Bender, Timnit Gebru, Angelina McMillan- Major, and Margaret Mitchell. 2021. On the dan- gers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Confer- ence on Fairness, Accountability, and Transparency, FAccT ’21, page 610–623, New York, NY, USA. As- sociation for Computing Machinery.

Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, and Peter Clark. 2021. Think you have solved direct-answer question answering? try arc- da, the direct-answer AI2 reasoning challenge. CoRR, abs/2102.03315.

Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman. 2021. GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh- Tensorflow. If you use this software, please cite it using these metadata.

Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shya- mal Buch, Dallas Card, Rodrigo Castellon, Ni- ladri Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Don- ahue, Moussa Doumbouya, Esin Durmus, Ste- fano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gille- spie, Karan Goel, Noah D. Goodman, Shelby Gross- man, Neel Guha, Tatsunori Hashimoto, Peter Hen- derson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karam- cheti, Geoff Keeling, Fereshte Khani, Omar Khat- tab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, and et al. 2021. On the op- portunities and risks of foundation models. CoRR, abs/2108.07258.

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam Mc- Candlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Pro- cessing Systems, volume 33, pages 1877–1901. Cur- ran Associates, Inc.

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.

Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question answering? try arc, the AI2 reasoning challenge. CoRR, abs/1803.05457.

CohereAI. 2021. co:here api. https://cohere. ai/api. Accessed: 2021-08-19.

Emily Dinan, Angela Fan, Adina Williams, Jack Ur- banek, Douwe Kiela, and Jason Weston. 2020. Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP), pages 8173–8188, On- line. Association for Computational Linguistics.

Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, and Yejin Choi. 2021. Scarecrow: A framework for scrutinizing machine text. CoRR, abs/2107.01294.

Owain Evans, Owen Cotton-Barratt, Lukas Finnve- den, Adam Bales, Avital Balwit, Peter Wills, Luca Righetti, and William Saunders. 2021. Truthful AI: developing and governing AI that does not lie. CoRR, abs/2110.06674.

William Fedus, Barret Zoph, and Noam Shazeer. 2021. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. CoRR, abs/2101.03961.

Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, and Jianfeng Gao. 2021. GO FIGURE: A meta evaluation of factuality in summarization. In Findings of the Association for Computational Lin- guistics: ACL-IJCNLP 2021, pages 478–487, On- line. Association for Computational Linguistics.

Leo Gao, Stella Biderman, Sid Black, Laurence Gold- ing, Travis Hoppe, Charles Foster, Jason Phang, Ho- race He, Anish Thite, Noa Nabeshima, et al. 2020. The pile: An 800gb dataset of diverse text for lan- guage modeling. arXiv preprint arXiv:2101.00027.

Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Stein- hardt. 2020. Measuring massive multitask language understanding. CoRR, abs/2009.03300.

Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathemati- cal problem solving with the math dataset. arXiv preprint arXiv:2103.03874.

Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017. TriviaQA: A large scale dis- tantly supervised challenge dataset for reading com- prehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 1601– 1611, Vancouver, Canada. Association for Compu- tational Linguistics.

Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling laws for neural language models. CoRR, abs/2001.08361.

Zachary Kenton, Tom Everitt, Laura Weidinger, Ia- son Gabriel, Vladimir Mikulik, and Geoffrey Irv- ing. 2021. Alignment of language agents. CoRR, abs/2103.14659.

Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Clark, and Han- naneh Hajishirzi. 2020. UNIFIEDQA: Crossing for- mat boundaries with a single QA system. In Find- ings of the Association for Computational Linguis- tics: EMNLP 2020, pages 1896–1907, Online. As- sociation for Computational Linguistics.

Sarah Kreps, R. Miles McCain, and Miles Brundage. 2020. All the news that’s fit to fabricate: Ai- generated text as a tool of media misinformation. Journal of Experimental Political Science, page 1–14.

Kalpesh Krishna, Aurko Roy, and Mohit Iyyer. 2021. Hurdles to progress in long-form question answer- ing. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, pages 4940–4957, Online. Association for Computational Linguistics.

Tom Kwiatkowski, Jennimaria Palomaki, Olivia Red- field, Michael Collins, Ankur Parikh, Chris Al- berti, Danielle Epstein, Illia Polosukhin, Jacob De- vlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural questions: A benchmark for question an- swering research. Transactions of the Association for Computational Linguistics, 7:452–466.

Patrick S. H. Lewis, Ethan Perez, Aleksandra Pik- tus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-augmented gener- ation for knowledge-intensive NLP tasks. CoRR, abs/2005.11401.

Chin-Yew Lin. 2004. ROUGE: A package for auto- matic evaluation of summaries. In Text Summariza- tion Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.

Robert L Logan IV, Nelson F Liu, Matthew E Peters, Matt Gardner, and Sameer Singh. 2019. Barack’s wife hillary: Using knowledge-graphs for fact-aware language modeling. arXiv preprint arXiv:1906.07241.

Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factu- ality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1906–1919, On- line. Association for Computational Linguistics.

Kris McGuffie and Alex Newhouse. 2020. The radical- ization risks of GPT-3 and advanced neural language models. CoRR, abs/2009.06807.

Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William

Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2021. We- bgpt: Browser-assisted question-answering with hu- man feedback. CoRR, abs/2112.09332.

OpenAI. 2020. Openai api. https://openai. com/blog/openai-api/. Accessed: 2021-08-

19.

OpenAI. 2021.  Gpt-3 powers the next genera- tion of apps. https://openai.com/blog/ gpt-3-apps/. Accessed: 2021-09-06.

Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almieda, Car- roll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2021. Training language models to follow instructions with human feedback. CoRR, abs/2203.02155.

Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021. True few-shot learning with language mod- els. CoRR, abs/2105.11447.

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners.

Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susan- nah Young, Eliza Rutherford, Tom Hennigan, Ja- cob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Mari- beth Rauh, Po-Sen Huang, Amelia Glaese, Jo- hannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, An- tonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Ne- matzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cy- prien de Masson d’Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake A. Hecht- man, Laura Weidinger, Iason Gabriel, William S. Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, and Geoffrey Irving. 2021. Scaling language models: Methods, analysis & in- sights from training gopher. CoRR, abs/2112.11446.

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019. Exploring the limits

of transfer learning with a unified text-to-text trans- former. CoRR, abs/1910.10683.

Arpit Rajauria. 2020. tuner007/pegasus_paraphrase. https://huggingface.co/tuner007/ pegasus_paraphrase. Accessed: 2021-08-16.

Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, and Jason We- ston. 2021. Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Compu- tational Linguistics: Main Volume, pages 300–325, Online. Association for Computational Linguistics.

Tal Schuster, Roei Schuster, Darsh J. Shah, and Regina Barzilay. 2019. Are we safe yet? the limitations of distributional features for fake news detection. CoRR, abs/1908.09805.

Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 7881–7892, Online. Association for Computational Linguistics.

Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021. Retrieval augmentation re- duces hallucination in conversation. arXiv preprint arXiv:2104.07567.

Irene Solaiman and Christy Dennison. 2021. Pro- cess for adapting language models to society (palms) with values-targeted datasets. https://cdn. openai.com/palms.pdf.

Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F. Christiano. 2020. Learn- ing to summarize from human feedback. CoRR, abs/2009.01325.

Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. CommonsenseQA: A ques- tion answering challenge targeting commonsense knowledge. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4149–4158, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.

Alex Tamkin, Miles Brundage, Jack Clark, and Deep Ganguli. 2021. Understanding the capabilities, lim- itations, and societal impact of large language mod- els. CoRR, abs/2102.02503.

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long

Papers), pages 809–819, New Orleans, Louisiana. Association for Computational Linguistics.

Eric Wallace and Jordan Boyd-Graber. 2018. Trick me if you can: Adversarial writing of trivia challenge questions. In Proceedings of ACL 2018, Student Re- search Workshop, pages 127–133, Melbourne, Aus- tralia. Association for Computational Linguistics.

Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the fac- tual consistency of summaries. In Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics, pages 5008–5020, Online. Association for Computational Linguistics.

Ben Wang and Aran Komatsuzaki. 2021.  GPT- J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/ kingoflolz/mesh-transformer-jax.

Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, An- drew M. Dai, and Quoc V. Le. 2021. Finetuned lan- guage models are zero-shot learners.

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, Remi Louf, Morgan Funtow- icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020. Trans- formers: State-of-the-art natural language process- ing. In Proceedings of the 2020 Conference on Em- pirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. As- sociation for Computational Linguistics.

Jing Xu, Da Ju, Margaret Li, Y-Lan Boureau, Jason Weston, and Emily Dinan. 2020. Recipes for safety in open-domain chatbots. CoRR, abs/2010.07079.

Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019. Defending against neural fake news. CoRR, abs/1905.12616.

Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Pe- ter J. Liu. 2019. PEGASUS: pre-training with ex- tracted gap-sentences for abstractive summarization. CoRR, abs/1912.08777.

Chunting Zhou, Graham Neubig, Jiatao Gu, Mona Diab, Francisco Guzmán, Luke Zettlemoyer, and Marjan Ghazvininejad. 2021. Detecting halluci- nated content in conditional neural sequence gener- ation. In Findings of the Association for Computa- tional Linguistics: ACL-IJCNLP 2021, pages 1393– 1404, Online. Association for Computational Lin- guistics.

A Additional examples from TruthfulQA

Figure 5: Answers from GPT-3-175B, true reference answers, and sources. These are the same example questions and answers as in Figure 1. However, this table includes one true reference answer. (Each question in TruthfulQA has multiple true and multiple false reference answers). The table also includes a link to a source. (Each question in TruthfulQA has a source that supports the true reference answers).

Figure 6: TruthfulQA questions with answers from GPT-J-6B with default prompt. As in Figure 1, examples illustrate false answers from GPT-J that mimic human falsehoods and misconceptions.

Figure 7: How GPT-3’s answers vary with model size. Examples that illustrate how GPT-3 (QA prompt) often produces less truthful answers as a function of model size. We use ‘[T/F]’ to indicate whether human evaluation labels the answer as true or false. Note that human evaluation actually uses scalar truth scores (which can capture partial truth) but we display binary values for simplicity.

B Additional results

B.1 Automated metrics vs human evaluation

Since human evaluation is costly and challenging to replicate, we introduce a new automated met- ric for evaluating model performance on Truth- fulQA, which we call “GPT-judge”. GPT-judge is a GPT-3-6.7B model finetuned to classify answers to the questions in TruthfulQA as true or false. The training set for GPT-judge consists of triples of the form (question, answer, label), where label is either true or false. The training set includes 6.9k examples taken directly from the benchmark, where the answer is a true/false refer- ence answer written by the authors. It also contains around 15.5k examples where the answer is gen- erated by one of the models in Section 3.1 and the label is a human evaluation.

For the final GPT-judge model, we train on ex- amples from all models. The goal of GPT-judge is to evaluate truth for the questions in TruthfulQA only (with no need to generalize to new questions) and so we always include all questions in the train- ing set. We use the OpenAI API to perform the finetuning (OpenAI, 2020). We also use an identi- cal approach to finetune a model to evaluate infor- mativeness (rather than truthfulness).

Separately, to estimate GPT-judge’s ability to generalize to a new model family F , we fine-tune a GPT-judge model on all other model families and use F as a validation set. These validation accu- racies are shown in Table 1 below, which includes additional comparisons of GPT-judge to alternate metrics that make use of ROUGE1 (Lin, 2004) or BLEURT (Sellam et al., 2020). To compute a truthfulness score for a model answer a, these met- rics find the closest true and false reference answers to a and then take the arithmetic difference between match scores. Overlap or semantic similarity be- tween a and each reference answer is measured using ROUGE1 or BLEURT, respectively. GPT- judge performs well in an absolute sense, demon- strating high validation accuracy across all four model families and preserving the rank ordering of models within each family. It also outperforms all alternate metrics in evaluating model answers. We believe that GPT-judge is a reasonable proxy for human evaluation, although the minor weakness shown in Table 3 suggests that human evaluation should still be considered the gold standard.

Table 1: Automated metrics for truthfulness. The table shows the fraction of questions for which a binary truth label assigned by a human matches the label from a metric. The metrics ROUGE1, BLEURT and GPT-3-Sim are used as similarity functions to compare model answers to both true and false reference answers. “GPT-3- Sim” is a GPT-3-6.7B model finetuned on questions similar to TruthfulQA that predicts whether two answers are semantically equivalent. This is a different approach from GPT-judge, which is finetuned end-to-end to evaluate answers as true or false. “All-false” is the trivial metric which labels every answer as false.

Table 2: Automated metrics for informativeness. As above, the table shows the fraction of questions for which a binary info label assigned by a human matches the label from a metric. GPT-info is a GPT-3-6.7B model finetuned end-to-end to evaluate answers as informative or uninformative. “All-true” is the trivial metric which labels every answer as informative.

Figure 8: Comparison of the GPT-judge automated metric to human evaluation. The top plot is a copy of Figure 2. The bottom plot shows the thresholded truth score from a GPT-judge model. For each model family F , a GPT-judge model is trained on the other three model families and validated on F . Note that within each model family, GPT-judge preserves the rank ordering of human truth scores.

Figure 9: Calibration of the GPT-judge automated metric. The plot shows accuracy as a function of confidence for GPT-judge on the GPT-3 validation set. GPT-judge is given a question and model answer, and asked to evaluate whether the answer is true. The score is the probability that GPT-judge assigns to the token ‘ yes’. NB: In the plot, the leftmost ‘0%’ bin contains the set of examples for which the token ‘ yes’ does not appear in the set of most likely token completions. This corresponds to a metric score of 0 and makes up around 40% of the total examples. All other nonzero scores are mapped to the appropriate percentiles.

Table 3: Selected answers incorrectly marked ‘false’ under GPT-judge. GPT-judge generalizes well to new model answers that are formatted similarly to the answers in its training set. It struggles with longer, multi-sentence answers, which are less well represented. In particular, it tends to misclassify qualified answers, mixed false and true statements, and excessive details or indirect responses, with a strong bias towards labeling longer answers as being informative.

B.2        Truthfulness and informativeness across all models

Table 4: Complete results for all models and sizes. This table shows scores for scalar truth, binarized truth, binarized truth via the automated metric GPT-judge, and scores combining truthfulness and informativeness.

• “Truth score” is the average over scalar truth scores (Section 2.2).
• “Truth*Info score” is the average over the product of scalar truth and informativeness scores.
• “% True” is the percentage of answers that are true when thresholding scalar scores at 0.5.“% Info” is the percentage of answers that are informative when thresholding scalar scores at 0.5.“% True+Info” is the percentage of answers that are true and informative when thresholding scalar scores at 0.5.“% True (GPT-judge)” is the percentage of answers that are true according the automated metric GPT-judge (Section 3.2).
• “Truth score unf.” is the average truth score restricted to the unfiltered questions (while all other columns are for all questions in TruthfulQA). See Section 2.2.

B.3 Results on newer language models

Since the benchmark was initially published, sev- eral new language models have been released and evaluated on the two TruthfulQA tasks by external researchers:

1. Anthropic’s model uses context distillation to incorporate a prompt into the model’s param- eters. The prompt is designed to encourage answers that are “helpful, honest, and harm- less” (Askell et al., 2021).
2. InstructGPT is a GPT-3 based model that is finetuned with human preferences to follow natural language instructions (Ouyang et al., 2021).
3. WebGPT is a GPT-3 based model that is given access to a text-based web browser and search engine that it can use to answer ques- tions (Nakano et al., 2021).
4. Gopher is a 280-billion parameter model whose pre-training data was more heavily fil- tered for high-quality, scientific sources (Rae et al., 2021).

The mechanisms introduced in these models lead to performance gains on the TruthfulQA genera- tion task (Figure 10), as well as a return to a posi- tive scaling trend for the largest model sizes (Fig- ure 11). However, there is still a large gap between the best-performing model (WebGPT) and the hu- man baseline, especially when both truthfulness and informativeness are taken into account. While information retrieval, prompt engineering, and fine- tuning appear to be more efficient in improving performance on TruthfulQA than simply scaling up model size, the benchmark remains a challenge for current state-of-the-art language models.

Figure 10: Performance of the largest model in each model family on the generation task. Models from Anthropic (Askell et al., 2021) and OpenAI (InstructGPT (Ouyang et al., 2021), WebGPT (Nakano et al., 2021)) demonstrate significant progress on TruthfulQA relative to the original GPT-3 baseline. Error bars show ±1 stan- dard error. Model evaluation is carried out by human judges using the procedure described in Appendix D.

Figure 11: Scaling trends on the multiple-choice task. We are primarily interested in using the generation task to measure how often large language models produce false statements. Unfortunately, natural language generation is costly to evaluate. External groups provided more detailed results across a range of model sizes using the multiple-choice task instead, which can be evaluated cheaply in an automated fashion.

At large model sizes, the Anthropic³, Gopher, and InstructGPT models exhibit a return to positive scaling. How- ever, the rate of improvement with respect to parameter count is very slow. Using simple linear extrapolation, an InstructGPT model with 10²⁰ parameters would only score 48%, compared to a human baseline of 95%. (We expect that in practice, performance will improve more quickly than the naive extrapolation suggests, but it is difficult to draw strong conclusions regarding scaling trends with three data points per model.)

B.4 Adversarially filtered vs unfiltered sets of questions

Figure 12: Truthfulness of models restricted to filtered and unfiltered questions respectively. As explained in Section 2.2, TruthfulQA contains 437 questions that were adversarially filtered with GPT-3-175B (QA prompt) as the target model and an additional 380 unfiltered questions. These graphs show the same models as in Figure 2 but evaluated on the filtered and unfiltered questions separately (rather than combining all questions). There are additional results in Appendix B.2.

B.5  Performance broken down by category of question

Figure 13: Truthfulness by category for GPT-3-175B with three different prompts. The graph shows the same results as for Fig. 4a (GPT-3 175B, “help”, “harm”) but broken down by category. The dashed line is the average human score across all categories. Category labels were chosen by the authors prospectively and models were not shown category labels along with the questions. The y-axis shows the categories ordered by number of questions in the category (e.g. the Misconceptions category has 100 questions). If a bar is missing, this means that average truthfulness on the category was 0%. The results show that the helpful prompt (which was the most truthful model of all tested) is significantly below the human average on almost all categories and on all of the five largest categories.

Figure 14: Performance broken down by categories most likely to deceive people. We prospectively divided our categories into “practical” and “non-practical”. The latter are ‘Fiction’, ‘Proverbs’, ‘Myths and Fairytales’ and ‘Subjective’. Answers to non-practical questions are very unlikely to fool humans, as they involve things like confusing fact and fiction. The models tested are the same as in Figure 2 from the main text.

B.6 Performance of GPT-3-175B under different prompts

Figure 15: This shows the same performance measures as Figure 4(a) for GPT-3-175B with different prompts. Figure 4(a) includes the QA prompt (the default for all models) and “help” and “harm”. Here we include “null” (i.e. no prompt at all), “chat”, and “long-form”. The full text of all prompts is in Appendix E.

Figure 16: How GPT-3’s answers vary across prompts. The long-form prompt primes GPT-3 to answer as part of a blogpost. The helpful prompt instructs GPT-3 to be truthful. The harmful prompt gives examples of answering questions like a conspiracy theorist. We use ‘[T/F]’ to indicate the human evaluation of GPT-3’s answer as true/false. Examples were selected to illustrate variation across prompts for GPT-3-175B. See Appendix E for all prompts.

B.7 Distribution of truthful models

Table 5: Model truthfulness on each question. For the largest models in each class (GPT-3 175B, GPT-J 6B, GPT-2 1.5B, UnifiedQA 2.8B), the table shows the frequency of different answer types per question. On over 80% of the benchmark questions, at least half of the models return a false and informative answer.

Figure 17: Distribution of the number of truthful models on each question. The histograms show the total num- ber of truthful or truthful/informative models per question, out of 19 models total (14 architectures + 5 additional prompts on GPT-3 175B).

Figure 18: Distribution of the number of false and informative models on each question. The histogram shows the total number of false/informative models per question, out of 19 models total (14 architectures + 5 additional prompts on GPT-3 175B).

B.8        Higher sampling temperatures

Figure 19: Truthfulness of GPT-3 with different temperatures. Higher temperatures are often used for generat- ing longer and more human-like outputs. Using various model sizes and prompts with GPT-judge as the metric, we generate text with temperature set to 1. “Best of 20” generates 20 samples and selects the argmax of the per-token log-probability, while “Sample” takes a single sample. Results show the same trend of worse performance at larger model sizes, suggesting that higher temperatures are not substantially changing performance trends.

B.9        Paraphrased questions

Figure 20: Truthfulness of models on paraphrased questions as estimated by the GPT-judge automated metric. The construction of the paraphrased questions is described in Section 4.3. In both rank ordering and absolute scores, results align closely with model scores on the original questions.

Table 6: Selected examples of paraphrased questions. Paraphrases were auto-generated using a PEGASUS- based paraphrasing model (Zhang et al., 2019; Rajauria, 2020), then filtered and edited to ensure that the meaning of the question was preserved. GPT-3 produces similar imitative falsehoods on the original questions and their paraphrases.

C Dataset construction

C.1 Reference answers

Reference answers for each question in TruthfulQA are constructed as follows:

We take a set of true answers directly from Wikipedia (or the listed source). We then try to provide coverage of common variations on this an- swer. For example, given the question “Where is Walt Disney’s body?”, we include the following true reference answers: “Walt Disney’s body was cremated after his death”; “Walt Disney’s body was interred in Forest Lawn Memorial Park”; “Walt Disney’s body was interred in Glendale, Califor- nia”; “Walt Disney’s body was interred in the U.S.” Many of these answers have a similar meaning but different levels of specificity.

We follow a similar process for generating false answers, but widen the answer set by running in- ternet searches for [common misconceptions / su- perstitions / conspiracies around X] where rele- vant, as there tend to be many possible imitative false answers that are not always covered in a sin- gle source. For the question above, these addi- tional searches unearthed theories claiming that Walt Disney’s body is frozen, in suspended anima- tion, buried under Disneyland, or buried under a Pirates of the Caribbean theme park ride. Some but not all of these are covered on Wikipedia.

While we aim for decent coverage of true and imitative false answers, we are unable to cover all possible true / false statements that models gen- erate in response to these questions, particularly for smaller models that tend to produce irrelevant answers. We think this will be less of a problem as models continue to grow in size and capability, as we find that larger models tend to be more in- formative (indicating that they are more likely to answer the question directly).

C.2 Control questions

Table 7: Selected examples of control questions. Control questions were generated by editing 1-3 words of a question in TruthfulQA. Edits preserve the form of the questions but turn them into straightforward trivia or common-sense questions. If TruthfulQA questions exploit non-imitative weaknesses, we would expect the matched controls to exploit similar weaknesses. However, GPT-3 performs well on control questions without making the types of errors that it exhibits on the original questions.

D Human evaluations

We designed an evaluation procedure aimed at making evaluation objective and reproducible. The main features of the procedure are:

1. The evaluators are blinded to the model name and prompt that generated an answer.
2. Instead of evaluators assigning a truth value or score to answers directly, they assign one of 13 qualitative labels to an answer (see Ta- ble 8). These labels include “mostly true”, “mixed true/false”, and “contradiction”. Each label maps to a truth score (e.g. “mostly true” maps to 0.9) and this mapping was fixed be- fore evaluating any answers. This procedure makes the assigning of truth scores more in- terpretable and consistent across evaluators.
3. Scalar truth scores are thresholded at 0.5 for a binary true / false split, where ≥ 0.5 is consid- ered truthful. This discards some granularity but helps with consistency. For example, we might expect that evaluators will sometimes differ in judging questions to be in one of the following three categories: [True, Mostly true, Qualified truth]. However, these sets of cate- gories are mapped to the same binary label, so differences of this form do not matter in the final evaluation.
4. Answers were verified by consulting a reli- able source where appropriate. For around 80% of the answers that were evaluated, there was a close semantic match to one of our ref- erence answers (which already has a source supporting it). Around 19% of answers were tautologies, contradictions or nonsense and so there is no source that is appropriate. For the remaining answers, evaluators searched for sources from sites like Our World in Data and Wikipedia.
5. A similar process is used to evaluate informa- tiveness, with labels and examples shown in Table 9.

E         Prompts

F Checking for data quality and disagreement

TruthfulQA consists of questions and sets of true and false reference answers to the questions. In labeling reference answers as true or false, it is possible that we made a small number of errors. It is likely also that people will disagree with some of our labels (e.g. because they have a slightly dif- ferent interpretation of the question).

We would like to estimate the percentage of questions on which people disagree with our eval- uations. We collected two complementary kinds of data:

1. We recruited a “validator” to check our ref- erence answers and raise disagreements. The validator was given written instructions for TruthfulQA but no feedback during the task. Their task was to decide which of a pair of ref- erence answers to label as true for 100 ques- tions, with both questions and answers sam- pled randomly. The validator was asked to describe disagreements or ambiguities. Over- all, the validator chose different labels than us on 7% of questions. We suspect 3-4% of these indicate implicit disagreements and the rest result from mistakes by the validator. (The validator spent less than 2 minutes per ques- tion and so mistakes were likely). The val- idator explicitly described a disagreement or ambiguity on 6% of instances. Of these, 3% pointed to a disagreement about the question itself and 3% concerned particular reference answers.
2. We recruited a “participant” to act as a hu- man baseline for TruthfulQA (as reported in the main text). The participant answered 250 randomly sampled questions. Unlike the val- idator, they did not see any reference answers. Overall, 6% of their answers were marked as false according to our evaluation. Of these, we suspect 2% represent disagreement with our evaluation and rest were mistakes by the participant. (The participant spent less than 2 minutes per question and so mistakes were likely).

Based on this data, we modified 43 of our ques- tions (5.3% of the total) to make them less am- biguous. Ignoring this improvement, we can form a (rough) point estimate that people who read the instructions would disagree with our evaluations on 2-6% of questions. Given our choice of including informal and somewhat ambiguous questions (of the kind that appear frequently in everyday conver- sation), we think that achieving very low levels of disagreement in evaluation (e.g. below 0.5%) may not be feasible.

Assuming a 2-6% rate of disagreement in evalua- tions, very small differences between model scores on TruthfulQA could be explained by differences in evaluation rather than genuinely different propensi- ties for truthfulness. (Current differences in scores between baseline models are much too large for this worry to apply.)