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Daily Arxiv

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AbGen: Evaluating Large Language Models in Ablation Study Design and Evaluation for Scientific Research

Created by
  • Haebom

Author

Yilun Zhao, Weiyuan Chen, Zhijian Xu, Manasi Patwardhan, Yixin Liu, Chengye Wang, Lovekesh Vig, Arman Cohan

Outline

AbGen is the first benchmark designed to evaluate the ability to design ablation studies for scientific research. It consists of 1,500 expert-annotated examples extracted from 807 NLP papers, and tasks LLMs with generating detailed ablation study designs for specific modules or processes in a given research context. The evaluation results on leading LLMs such as DeepSeek-R1-0528 and o4-mini show significant performance differences between these models and experts in terms of the importance, fidelity, and soundness of ablation study design. Furthermore, current automated evaluation methods show significant differences compared to human evaluations, suggesting that they are unreliable for this task. To investigate this further, we developed AbGen-Eval, a meta-evaluation benchmark designed to evaluate the reliability of common automated evaluation systems used to measure LLM performance in this task. AbGen-Eval examines a variety of LLM-as-Judge systems, providing insights into the development of more effective and reliable LLM-based evaluation systems for complex scientific tasks.

Takeaways, Limitations

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Takeaways: AbGen benchmark provides a new standard for evaluating the ablation study design capability of LLMs. It clearly shows the performance limitations of LLMs and suggests future research directions. It raises the reliability issue of automatic evaluation systems and emphasizes the need for developing better evaluation systems. AbGen-Eval contributes to research to improve the reliability of LLM-based evaluation systems.
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Limitations: Demonstrates the lack of ablation study design capabilities of current mainstream LLMs. Reveals the difficulty of evaluating LLM performance due to the lack of reliability of automated evaluation systems. Additional research may be needed on the dataset size and diversity of the AbGen benchmark. Further research is needed to determine whether the insights provided by AbGen-Eval can be generalized to all complex scientific tasks.
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