A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists

A Nature Chemistry paper introduces ChemBench, a chemistry test for language models.

Author
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martiño Ríos-García and many co-authors, Nature Chemistry
Published
20 May 2025
Kind
Peer-reviewed open-access paper introducing a benchmark, with a human expert baseline
Original
www.nature.com/articles/s41557-025-01815-x
Subject
AI put to use

What it is

A large author team led by Kevin Maik Jablonka's group presents ChemBench. It is an automated framework for testing chemical knowledge and reasoning in language models. The paper describes the question corpus, the evaluation method and a survey of expert chemists. It aims to measure ability and expose safety-relevant weaknesses.

What it says

What evidence it gives

The paper offers scores for many models on 2,788 questions. Each was reviewed by at least two scientists. A smaller 236 question subset was answered by volunteers, with tool use allowed on part. It reports per-topic results, a preference test on 1,000 questions and a confidence analysis. Data and code are public.

Interests and limits

The authors made the benchmark and run its leaderboard. One author has been a paid contractor for OpenAI and others worked for Stability.AI. Only 19 experts took part. Of 16 who gave a background, 13 held master's degrees and were doing PhDs. Scoring was strict, and API refusals may have lowered some scores. Supplementary notes and figure images were not read.