Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim $\rightarrow$ Evidence Reasoning (2506.08235v1)

Abstract: LLMs are increasingly being used for complex research tasks such as literature review, idea generation, and scientific paper analysis, yet their ability to truly understand and process the intricate relationships within complex research papers, such as the logical links between claims and supporting evidence remains largely unexplored. In this study, we present CLAIM-BENCH, a comprehensive benchmark for evaluating LLMs' capabilities in scientific claim-evidence extraction and validation, a task that reflects deeper comprehension of scientific argumentation. We systematically compare three approaches which are inspired by divide and conquer approaches, across six diverse LLMs, highlighting model-specific strengths and weaknesses in scientific comprehension. Through evaluation involving over 300 claim-evidence pairs across multiple research domains, we reveal significant limitations in LLMs' ability to process complex scientific content. Our results demonstrate that closed-source models like GPT-4 and Claude consistently outperform open-source counterparts in precision and recall across claim-evidence identification tasks. Furthermore, strategically designed three-pass and one-by-one prompting approaches significantly improve LLMs' abilities to accurately link dispersed evidence with claims, although this comes at increased computational cost. CLAIM-BENCH sets a new standard for evaluating scientific comprehension in LLMs, offering both a diagnostic tool and a path forward for building systems capable of deeper, more reliable reasoning across full-length papers.

• The paper introduces CLAIM-BENCH, a benchmark to assess LLMs' ability to extract and validate scientific claim-evidence pairs from extensive research texts.
• Using Single-Pass, Three-Pass, and One-by-One prompting, the study reveals that closed-source models like GPT-4 and Claude outperform open-source counterparts in precision and recall.
• The findings highlight LLM limitations in processing complex scientific content, suggesting iterative prompting strategies to enhance multi-step reasoning and long-context comprehension.

Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim-Evidence Reasoning

The paper explores the capabilities and limitations of LLMs in accurately understanding and processing scientific texts, specifically focusing on the task of claim-evidence extraction and validation. The authors propose CLAIM-BENCH, a comprehensive benchmark developed to evaluate how well LLMs can identify and reason about the intricate relationships between scientific claims and supporting evidence within full-length research papers. This paper involves an evaluation of six distinct LLMs with contexts spanning over 128K tokens, examining model-specific strengths, weaknesses, and overarching patterns in scientific comprehension.

Key Findings and Experimental Setup

The paper utilizes three distinct prompting strategies to dissect LLM performances: Single-Pass, which processes the entire document in one go; Three-Pass, a sequential approach dividing the task into claim identification, evidence retrieval, and conclusion analysis; and One-by-One Pass, where claims are individually extracted and evaluated against evidence. The authors assess six models, including GPT-4, Claude 3.5, Gemini-Exp_1114, and open-source models like LLaMA, Ministral, and Phi.

Experimentation results showed that closed-source models like GPT-4 and Claude consistently outperformed open-source counterparts in precision and recall across claim-evidence identification tasks. Notably, the Three-Pass and One-by-One prompting methods led to significant improvements in identifying and connecting dispersed evidence with claims, albeit at a significant computational cost.

The authors found that LLMs faced substantial limitations in processing complex scientific content. For example, while closed-source models achieved high precision in claim extraction, identifying and verifying dispersed evidence across lengthy documents proved challenging. Hence, they often showcased higher recall at the cost of precision. Additionally, the paper observed that larger models demonstrated robust recall even with lengthy documents, especially when utilizing iterative prompting strategies, whereas smaller models experienced notable performance reductions under similar conditions.

Implications and Future Developments

CLAIM-BENCH sets a new standard for evaluating scientific comprehension in LLMs, offering insights that hold significant implications for both practical and theoretical AI applications. From a practical perspective, enhancing LLMs to effectively validate claim-evidence pairs could transform research methodologies, influence peer review processes, and expedite new scientific discoveries. For instance, reliable claim validation could automate or assist in scientifically accurate hypothesis generation and experiment design.

Theoretically, developing AI systems capable of deeper, more reliable reasoning across scientific texts could lead to advances in multi-step reasoning, long-context understanding, and more sophisticated machine comprehension models. Institutes and researchers could leverage these insights for creating retrieval-augmented laboratory assistants and cross-paper evidence graphs, thereby pushing the frontier of AI-driven research assistance.

Conclusion

In conclusion, this paper highlights the critical need for benchmarks like CLAIM-BENCH that challenge LLMs to operate beyond surface-level comprehension, addressing the inherent challenges of high-level scientific reasoning. The findings suggest that while current LLM architectures exhibit promising capabilities in certain tasks, substantial work remains to augment their proficiency in nuanced scientific reasoning and comprehension. Future developments may focus on refining LLM architectures and prompting strategies, thereby facilitating AI systems that integrate seamlessly into advanced scientific workflows.