Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical Prediction
We propose the Identity-Blinded Reasoning Diagnostic framework to evaluate chemical prediction models when target entity identity information is hidden.
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Key points
- LLMs have achieved strong performance on chemical prediction benchmarks.
- However, existing evaluation methods typically provide target entity identity information directly, making it difficult to determine whether model predictions arise from transferable structure-property reasoning capabilities or rely on target-related contextual information or memory retrieval.
- We evaluate this framework across six tasks spanning odor prediction, molecular property prediction, retrosynthesis, and reaction yield prediction.
- These results indicate that high benchmark accuracy in chemical prediction tasks does not necessarily imply that LLMs possess reliable structure-property reasoning capabilities.
Sources (1)
- [1]Beyond Benchmark Accuracy: A Diagnostic Test for LLM Chemical PredictionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 10:03 AM
We propose the Identity-Blinded Reasoning Diagnostic framework to evaluate chemical prediction models when target entity identity information is hidden.
LLMs have achieved strong performance on chemical prediction benchmarks.
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