  The performance of large language models (LLMs) on existing reasoning
benchmarks has significantly improved over the past years. In response, we
present JEEBench, a considerably more challenging benchmark dataset for
evaluating the problem solving abilities of LLMs. We curate 515 challenging
pre-engineering mathematics, physics and chemistry problems from the highly
competitive IIT JEE-Advanced exam. Long-horizon reasoning on top of deep
in-domain knowledge is essential for solving problems in this benchmark. Our
evaluation on various open-source and proprietary models reveals that the
highest performance, even after using techniques like self-consistency,
self-refinement and chain-of-thought prompting, is less than 40%. The typical
failure modes of GPT-4, the best model, are errors in algebraic manipulation,
difficulty in grounding abstract concepts into mathematical equations
accurately and failure in retrieving relevant domain-specific concepts. We also
observe that by mere prompting, GPT-4 is unable to assess risk introduced by
negative marking for incorrect answers. For this, we develop a post-hoc
confidence-thresholding method over self-consistency, which enables effective
response selection. We hope that our challenging benchmark will guide future
re-search in problem-solving using LLMs.
