Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain
Qing Huang, Zhenyu Wan, Zhenchang Xing, Changjing Wang, Jieshan Chen,, Xiwei Xu, Qinghua Lu

TL;DR
This paper introduces a knowledge-guided query clarification method using LLMs and knowledge graphs to improve API recommendation accuracy, efficiency, and fluency, overcoming limitations of previous KG-based approaches.
Contribution
It proposes a novel AI chain framework that integrates LLMs with knowledge graphs for enhanced API query clarification, addressing OOV issues and noise filtering.
Findings
Significant improvement in MRR over baselines, up to 63.9%.
High scores (close to 5) for each unit in the AI chain.
Knowledge guidance and pathfinding are crucial, boosting MAP by over 20%.
Abstract
API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge graph (KG)-based, but limitations include out-of-vocabulary (OOV) failures and rigid question templates. To address these limitations, we propose a novel knowledge-guided query clarification approach for API recommendation that leverages a large language model (LLM) guided by KG. We utilize the LLM as a neural knowledge base to overcome OOV failures, generating fluent and appropriate clarification questions and options. We also leverage the structured API knowledge and entity relationships stored in the KG to filter out noise, and transfer the optimal clarification path from KG to the LLM, increasing the efficiency of the clarification process. Our approach is designed as an AI chain that consists of five steps,…
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Taxonomy
TopicsTopic Modeling · Data Quality and Management · Natural Language Processing Techniques
