Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Abstract
Executing computer programs described in natural language has long been a pursuit of computer science. With the advent of enhanced natural language understanding capabilities exhibited by large language models (LLMs), the path toward this goal has been illuminated. In this paper, we seek to examine the capacity of present-day LLMs to comprehend and execute algorithms outlined in natural language. We established an algorithm test set sourced from Introduction to Algorithm, a well-known textbook that contains many representative widely-used algorithms. To systematically assess LLMs’ code execution abilities, we selected 30 algorithms, generated 300 random-sampled instances in total, and evaluated whether popular LLMs can understand and execute these algorithms. Our findings reveal that LLMs, notably GPT-4, can effectively execute programs described in natural language, as long as no heavy numeric computation is involved. We believe our findings contribute to evaluating LLMs’ code execution abilities and would encourage further investigation and application for the computation power of LLMs.- Anthology ID:
- 2024.lrec-main.596
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 6752–6837
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.596
- DOI:
- Cite (ACL):
- Xin Zheng, Qiming Zhu, Hongyu Lin, Yaojie Lu, Xianpei Han, and Le Sun. 2024. Executing Natural Language-Described Algorithms with Large Language Models: An Investigation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6752–6837, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Executing Natural Language-Described Algorithms with Large Language Models: An Investigation (Zheng et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.lrec-main.596.pdf