EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario

Sijie Li, Sha Li, Hao Zhang, Shuyang Li, Kai Chen, Jianyong Yuan, Yi Cao, Lvqing Yang


Abstract
In recent years, Large Language Models (LLMs) have demonstrated exceptional performance in code-generation tasks. However, under enterprise scenarios where private APIs are pre-built, general LLMs often fail to meet expectations. Existing approaches are confronted with drawbacks of high resource consumption and inadequate handling of multi-API tasks. To address these challenges, we propose EpiGEN, an Efficient multi-Api code GENeration framework under enterprise scenario. It consists of three core modules: Task Decomposition Module (TDM), API Retrieval Module (ARM), and Code Generation Module (CGM), in which Langchain played an important role. Through a series of experiments, EpiGEN shows good acceptability and readability, compared to fully fine-tuned LLM with a larger number of parameters. Particularly, in medium and hard level tasks, the performance of EpiGEN on a single-GPU machine even surpasses that of a fully fine-tuned LLM that requires multi-GPU configuration. Generally, EpiGEN is model-size agnostic, facilitating a balance between the performance of code generation and computational requirements.
Anthology ID:
2024.lrec-main.548
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:
6206–6215
Language:
URL:
https://aclanthology.org/2024.lrec-main.548
DOI:
Bibkey:
Cite (ACL):
Sijie Li, Sha Li, Hao Zhang, Shuyang Li, Kai Chen, Jianyong Yuan, Yi Cao, and Lvqing Yang. 2024. EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 6206–6215, Torino, Italia. ELRA and ICCL.
Cite (Informal):
EpiGEN: An Efficient Multi-Api Code GENeration Framework under Enterprise Scenario (Li et al., LREC-COLING 2024)
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PDF:
https://aclanthology.org/2024.lrec-main.548.pdf