Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models

• URL: http://arxiv.org/abs/2305.15074v3
• Date: Mon, 23 Oct 2023 11:55:58 GMT
• Title: Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
• Authors: Daman Arora, Himanshu Gaurav Singh, Mausam
• Abstract summary: We present 515Bench, a more challenging benchmark dataset for evaluating the problem solving abilities of large language models (LLMs) We curate challenging pre-engineering mathematics, physics and chemistry problems from the highly competitive IIT-Advanced exam. 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%.
• Score: 23.344490944210456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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