LLMs ground answers in early context far more than later context, and chain-of-thought prompting or reasoning models reduce contextual grounding rather than improving it.
Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval
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abstract
This study leverages optimized context retrieval to enhance open-source Large Language Models (LLMs) for cost-effective, high performance healthcare AI. We demonstrate that this approach achieves state-of-the-art accuracy on medical question answering at a fraction of the cost of proprietary models, significantly improving the cost-accuracy Pareto frontier on the MedQA benchmark. Key contributions include: (1) OpenMedQA, a novel benchmark revealing a performance gap in open-ended medical QA compared to multiple-choice formats; (2) a practical, reproducible pipeline for context retrieval optimization; and (3) open-source resources (Prompt Engine, CoT/ToT/Thinking databases) to empower healthcare AI development. By advancing retrieval techniques and QA evaluation, we enable more affordable and reliable LLM solutions for healthcare.
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cs.CL 1years
2025 1verdicts
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"Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models
LLMs ground answers in early context far more than later context, and chain-of-thought prompting or reasoning models reduce contextual grounding rather than improving it.