AbGen: Evaluating Large Language Models in Ablation Study Design and
  Evaluation for Scientific Research
Authors: name: Yilun Zhao, name: Weiyuan Chen, name: Zhijian Xu, name: Manasi Patwardhan, name: Yixin Liu, name: Chengye Wang, name: Lovekesh Vig, name: Arman Cohan

We introduce AbGen, the first benchmark designed to evaluate the capabilities
of LLMs in designing ablation studies for scientific research. AbGen consists
of 1,500 expert-annotated examples derived from 807 NLP papers. In this
benchmark, LLMs are tasked with generating detailed ablation study designs for
a specified module or process based on the given research context. Our
evaluation of leading LLMs, such as DeepSeek-R1-0528 and o4-mini, highlights a
significant performance gap between these models and human experts in terms of
the importance, faithfulness, and soundness of the ablation study designs.
Moreover, we demonstrate that current automated evaluation methods are not
reliable for our task, as they show a significant discrepancy when compared to
human assessment. To better investigate this, we develop AbGen-Eval, a
meta-evaluation benchmark designed to assess the reliability of commonly used
automated evaluation systems in measuring LLM performance on our task. We
investigate various LLM-as-Judge systems on AbGen-Eval, providing insights for
future research on developing more effective and reliable LLM-based evaluation
systems for complex scientific tasks.