Evaluating In-Context Learning of Libraries for Code Generation

Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi


Abstract
Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Recent work has shown that large proprietary LLMs can learn novel library usage in-context from demonstrations. These results raise several open questions: whether demonstrations of library usage is required, whether smaller (and more open) models also possess such capabilities, etc. In this work, we take a broader approach by systematically evaluating a diverse array of LLMs across three scenarios reflecting varying levels of domain specialization to understand their abilities and limitations in generating code based on libraries defined in-context. Our results show that even smaller open-source LLMs like Llama-2 and StarCoder demonstrate an adept understanding of novel code libraries based on specification presented in-context. Our findings further reveal that LLMs exhibit a surprisingly high proficiency in learning novel library modules even when provided with just natural language descriptions or raw code implementations of the functions, which are often cheaper to obtain than demonstrations. Overall, our results pave the way for harnessing LLMs in more adaptable and dynamic coding environments.
Anthology ID:
2024.naacl-long.161
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2908–2926
Language:
URL:
https://aclanthology.org/2024.naacl-long.161
DOI:
Bibkey:
Cite (ACL):
Arkil Patel, Siva Reddy, Dzmitry Bahdanau, and Pradeep Dasigi. 2024. Evaluating In-Context Learning of Libraries for Code Generation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2908–2926, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Evaluating In-Context Learning of Libraries for Code Generation (Patel et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.161.pdf
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 2024.naacl-long.161.copyright.pdf