@inproceedings{wang-etal-2025-exploracoder,
title = "{E}xplora{C}oder: Advancing Code Generation for Multiple Unseen {API}s via Planning and Chained Exploration",
author = "Wang, Yunkun and
Zhang, Yue and
Qin, Zhen and
Zhi, Chen and
Li, Binhua and
Huang, Fei and
Li, Yongbin and
Deng, Shuiguang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.887/",
doi = "10.18653/v1/2025.acl-long.887",
pages = "18124--18145",
ISBN = "979-8-89176-251-0",
abstract = "Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhaustively retrain LLMs with new API knowledge. This limitation hampers LLMs from solving programming problems which require newly introduced or privately maintained libraries. Inspired by exploratory programming paradigm in human behavior, we propose **ExploraCoder**, a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by (1) planning a complex problem into several API invocation subtasks, and (2) experimenting with correct API usage at intermediate steps through a novel chain-of-API-exploration. We conduct evaluation on program synthesizing tasks involving complex API interactions. Experimental results demonstrate that ExploraCoder significantly improves performance for models lacking prior API knowledge, achieving absolute increases of up to 11.99{\%} over retrieval-based approaches and 17.28{\%} over pretraining-based methods in pass@10."
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<abstract>Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhaustively retrain LLMs with new API knowledge. This limitation hampers LLMs from solving programming problems which require newly introduced or privately maintained libraries. Inspired by exploratory programming paradigm in human behavior, we propose **ExploraCoder**, a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by (1) planning a complex problem into several API invocation subtasks, and (2) experimenting with correct API usage at intermediate steps through a novel chain-of-API-exploration. We conduct evaluation on program synthesizing tasks involving complex API interactions. Experimental results demonstrate that ExploraCoder significantly improves performance for models lacking prior API knowledge, achieving absolute increases of up to 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10.</abstract>
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%0 Conference Proceedings
%T ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration
%A Wang, Yunkun
%A Zhang, Yue
%A Qin, Zhen
%A Zhi, Chen
%A Li, Binhua
%A Huang, Fei
%A Li, Yongbin
%A Deng, Shuiguang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-exploracoder
%X Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhaustively retrain LLMs with new API knowledge. This limitation hampers LLMs from solving programming problems which require newly introduced or privately maintained libraries. Inspired by exploratory programming paradigm in human behavior, we propose **ExploraCoder**, a training-free framework that empowers LLMs to invoke multiple unseen APIs in code solution by (1) planning a complex problem into several API invocation subtasks, and (2) experimenting with correct API usage at intermediate steps through a novel chain-of-API-exploration. We conduct evaluation on program synthesizing tasks involving complex API interactions. Experimental results demonstrate that ExploraCoder significantly improves performance for models lacking prior API knowledge, achieving absolute increases of up to 11.99% over retrieval-based approaches and 17.28% over pretraining-based methods in pass@10.
%R 10.18653/v1/2025.acl-long.887
%U https://aclanthology.org/2025.acl-long.887/
%U https://doi.org/10.18653/v1/2025.acl-long.887
%P 18124-18145
Markdown (Informal)
[ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration](https://aclanthology.org/2025.acl-long.887/) (Wang et al., ACL 2025)
ACL
- Yunkun Wang, Yue Zhang, Zhen Qin, Chen Zhi, Binhua Li, Fei Huang, Yongbin Li, and Shuiguang Deng. 2025. ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18124–18145, Vienna, Austria. Association for Computational Linguistics.