Enterprise AI Analysis
Counting Clues: A Lightweight Probabilistic Baseline Can Match an LLM
This paper introduces the Frequency-Based Probabilistic Ranker (FBPR), a lightweight method for medical diagnosis that uses smoothed Naive Bayes over concept-diagnosis co-occurrence statistics from large corpora. It shows that FBPR achieves performance comparable to large language models (LLMs) like OLMo Instruct 7B and LLaMA 65B when both are trained on the same corpus (Dolma/RedPajama). The methods often get different questions correct, suggesting complementary strengths. This highlights the value of explicit probabilistic baselines as a reference point and a signal for potential hybridization.
Key Performance Insights for Enterprise
Understanding the real-world accuracy of different AI approaches in critical domains like medical diagnosis reveals opportunities for robust, hybrid AI systems.
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FBPR Accuracy (Dolma)
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LLM Accuracy (OLMo)
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FBPR Accuracy (RedPajama)
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LLM Accuracy (LLaMA)
Deep Analysis & Enterprise Applications
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Probabilistic Reasoning
LLM Performance & Baselines
Probabilistic Reasoning in AI
The research explores how much of LLMs' medical QA performance reflects corpus-level co-occurrence statistics versus structured probabilistic reasoning. It introduces FBPR as a baseline to address this.
Key Concepts: Frequency-Based Probabilistic Ranker (FBPR), Naive Bayes, Co-occurrence Statistics, Probabilistic Reasoning
LLM Performance Benchmarking
The study evaluates FBPR against LLMs (OLMo, LLaMA) on MedQA diagnosis tasks. FBPR achieves comparable accuracy, indicating that simpler, frequency-based methods can account for a significant portion of benchmark performance.
Key Concepts: MedQA Benchmark, OLMo Instruct 7B, LLaMA 65B, Benchmark Performance
46.7%
FBPR (Dolma) Accuracy on MedQA Diagnosis Subset
Frequency-Based Probabilistic Ranker (FBPR) Pipeline
Extract Clinical Concepts
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Retrieve Counts from Corpora
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Calculate Naive-Bayes Score
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Select Most Likely Diagnosis
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