Title: ProfBench: Multi-Domain Rubrics requiring Professional Knowledge to Answer and Judge
Primary area: datasets and benchmarks
Keywords: expert-annotated, professional knowledge, llm judge, rubric evaluation

Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to tasks like mathematics, programming, and short-form question-answering. However, many real-world applications require evaluating LLMs in processing professional documents, synthesizing information, and generating comprehensive reports in response to user queries. We introduce ProfBench: a set of over 7000 response-criterion pairs as evaluated by human-experts with professional knowledge across Physics PhD, Chemistry PhD, Finance MBA and Consulting MBA.  We build robust and affordable LLM-Judges to evaluate ProfBench rubrics, by mitigating self-enhancement bias and reducing the cost of evaluation by 2-3 orders of magnitude, to make it fair and accessible to the broader community. Our findings reveal that ProfBench poses significant challenges even for state-of-the-art LLMs, with top-performing models like GPT-5-high achieving only 65.9\% overall performance. Furthermore, we identify notable performance disparities between proprietary and open-weight models and provide insights into the role that extended thinking plays in addressing complex, professional-domain tasks.

TL;DR: We curate a PhD/MBA-level human-annotated rubrics dataset across Physics, Chemistry, Finance and Consulting with >7000 criterion-response pairs and introduce methods to mitigate bias and high cost of evaluation to make it fair and accessible to all.