CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning
Hao Cui Zahra Shamsi Gowoon Cheon Xuejian Ma Shutong Li Maria Tikhanovskaya Peter Christian Norgaard Nayantara Mudur Martyna Beata Plomecka Paul Raccuglia Yasaman Bahri Victor V. Albert Pranesh Srinivasan Haining Pan Philippe Faist Brian A Rohr Michael J. Statt Dan Morris Drew Purves Elise Kleeman et al. (10 additional authors not shown)
Scientific problem-solving involves synthesizing information while applying expert  knowledge.   We  introduce  CURIE,  a  scientific  long-Context  Understanding, Reasoning, and Information Extraction benchmark to measure the potential of Large Language Models (LLMs) in scientific problem-solving and assisting scientists in realistic workflows. This benchmark introduces ten challenging tasks with a total of 580 problems and solution pairs curated by experts in six disciplines - materials science, condensed matter physics, quantum computing, geo-spatial analysis, biodiversity, and proteins - covering both experimental and theoretical work-flows in science. We evaluate a range of closed and open LLMs on tasks in CURIE which requires domain expertise, comprehension of long in-context information,and multi-step reasoning.  While Gemini Flash 2.0 and Claude-3 show consistent high comprehension across domains, the popular GPT-4o and command-R+ fail dramatically on protein sequencing tasks. With the best performance at 32% there is much room for improvement for all models. We hope that insights gained from CURIE can guide the future development of LLMs in sciences. Links to the data and evaluation code are in https://github.com/google/curie
science, LLMs, evaluation, benchmark, long-context
CURIE is a long context comprehension benchmark to asses the ability of LLMs in assisting in realistic scientific workflows requiring deep expertise.