Enterprise AI Analysis
Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy
This study investigates hallucinations in LLMs for bibliographic recommendation, proposing that citation frequency acts as a proxy for training data redundancy. It finds that highly cited papers show lower hallucination rates, and bibliographic information becomes nearly verbatimly memorized beyond approximately 1,000 citations, indicating a threshold where generalization shifts to memorization.
Executive Impact at a Glance
Key metrics derived from the research highlight critical areas for enterprise AI strategy and implementation.
Accuracy Improvement
Memorization Threshold
Domain Variance
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Problem Identification
Methodology
Key Findings
This section details the core problem of hallucinations in LLMs for bibliographic recommendations, illustrating it with a specific example of a fabricated reference.
The Challenge: Fabricated References
Non-Existent Papers GeneratedLLMs can generate highly plausible but entirely fabricated academic references, which poses a critical issue for reliability. This often involves merging elements from multiple real papers into a coherent, yet false, citation.
Process of Hallucination in Bibliographic Recommendation
LLM Prompted for Papers
→
Accesses Training Corpus
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Identifies Patterns/Data
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Synthesizes Plausible Info
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Generates Fabricated Citation
The study employed GPT-4.1 to generate bibliographic records across diverse computer science domains, manually verifying factual consistency and correlating it with citation counts using Sentence-BERT for semantic similarity.
| Score | Description | Example |
|---|---|---|
| 2 (Correct) | Completely accurate bibliographic information. | Liu et al. (2021) Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. |
| 1 (Partially Hallucinated) | Some metadata inaccurate (author, journal, year). | Ma et al. (2022) Mega: Moving average equipped gated attention (incorrect page numbers). |
| 0 (Completely Hallucinated) | Non-existent paper. | Kossen et al. (2023) Self-Attention for Raw Numerical Tabular Data. |
Metric: Citation Count as Proxy
818 Median Citation CountCitation count was used as a proxy for training data redundancy, hypothesizing that higher redundancy (more citations) correlates with lower hallucination rates.
The research revealed a strong correlation between citation count and factual accuracy, with a clear memorization threshold around 1,000 citations, and varying hallucination rates across domains.
Citation-Accuracy Correlation
0.75 Correlation (r)A strong positive correlation (r=0.75, p<.001) found between log-transformed citation counts and cosine similarity, indicating higher factual accuracy for highly cited papers.
Memorization Threshold in Action
Analysis showed that beyond approximately 1,000 citations (log(citation) ≈ 7), bibliographic information is almost verbatimly retained in the model. This suggests a shift from generalization to memorization for highly redundant data.
Domain-Specific Hallucinations
Varied Hallucination RatesHallucination rates differ significantly across research domains. Domains like Vision Transformer and Diffusion Model showed higher accuracy, while RAG and LoRA had lower scores, reflecting domain popularity and data redundancy.
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