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.- Anthology ID:
- 2023.emnlp-main.468
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7527–7543
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.468
- DOI:
- 10.18653/v1/2023.emnlp-main.468
- Cite (ACL):
- Daman Arora, Himanshu Singh, and Mausam. 2023. Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7527–7543, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models (Arora et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2023.emnlp-main.468.pdf