|
--- |
|
license: mit |
|
task_categories: |
|
- question-answering |
|
language: |
|
- en |
|
tags: |
|
- chemistry |
|
- physics |
|
- mathematics |
|
pretty_name: jeebench |
|
size_categories: |
|
- n<1K |
|
--- |
|
|
|
# JEEBench(EMNLP 2023) |
|
Repository for the code and dataset for the paper: "Have LLMs Advanced Enough? A Harder Problem Solving Benchmark For Large Language Models" accepted in EMNLP 2023 as a Main conference paper. |
|
https://aclanthology.org/2023.emnlp-main.468/ |
|
|
|
|
|
## Citation |
|
|
|
If you use our dataset in your research, please cite it using the following |
|
```latex |
|
@inproceedings{arora-etal-2023-llms, |
|
title = "Have {LLM}s Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models", |
|
author = "Arora, Daman and |
|
Singh, Himanshu and |
|
{Mausam}", |
|
editor = "Bouamor, Houda and |
|
Pino, Juan and |
|
Bali, Kalika", |
|
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", |
|
month = dec, |
|
year = "2023", |
|
address = "Singapore", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2023.emnlp-main.468", |
|
doi = "10.18653/v1/2023.emnlp-main.468", |
|
pages = "7527--7543", |
|
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.", |
|
} |
|
``` |
