MolErr2Fix:Benchmarking LLM Trustworthiness in Chemistry via Modular Error Detection, Localization, Explanation, and Revision

2025-08-26

AI Summary

Large language models (LLMs) frequently generate chemically inaccurate descriptions in molecular science and lack capabilities for error detection and interpretable correction, undermining their scientific reliability. Method: We introduce MolErr2Fix, the first fine-grained benchmark for diagnosing and correcting chemical reasoning errors, comprising 1,193 human-annotated instances covering structural and semantic errors. It features a novel quadruple annotation schema—error type, location, explanation, and correction—integrating structured error taxonomy with domain-specific chemical knowledge validation. The benchmark includes modular task designs and an open-source evaluation API. Contribution/Results: Comprehensive evaluation of mainstream LLMs reveals significant deficiencies in error localization and correction. MolErr2Fix effectively exposes model weaknesses, serving as a critical evaluation tool and data foundation for advancing trustworthy chemical reasoning in LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Language Models (LLMs) have shown growing potential in molecular sciences, but they often produce chemically inaccurate descriptions and struggle to recognize or justify potential errors. This raises important concerns about their robustness and reliability in scientific applications. To support more rigorous evaluation of LLMs in chemical reasoning, we present the MolErr2Fix benchmark, designed to assess LLMs on error detection and correction in molecular descriptions. Unlike existing benchmarks focused on molecule-to-text generation or property prediction, MolErr2Fix emphasizes fine-grained chemical understanding. It tasks LLMs with identifying, localizing, explaining, and revising potential structural and semantic errors in molecular descriptions. Specifically, MolErr2Fix consists of 1,193 fine-grained annotated error instances. Each instance contains quadruple annotations, i.e,. (error type, span location, the explanation, and the correction). These tasks are intended to reflect the types of reasoning and verification required in real-world chemical communication. Evaluations of current state-of-the-art LLMs reveal notable performance gaps, underscoring the need for more robust chemical reasoning capabilities. MolErr2Fix provides a focused benchmark for evaluating such capabilities and aims to support progress toward more reliable and chemically informed language models. All annotations and an accompanying evaluation API will be publicly released to facilitate future research.

Problem

• Assessing LLM trustworthiness in chemical error detection
• Evaluating error localization and explanation in molecular descriptions
• Benchmarking LLM capabilities for chemical error correction

Innovation

• Benchmark for LLM error detection and correction in chemistry
• Quadruple annotations for error type, location, explanation, correction
• Fine-grained chemical understanding evaluation with 1,193 instances