Assessing the Answerability of Queries in Retrieval-Augmented Code Generation

• URL: http://arxiv.org/abs/2411.05547v2
• Date: Mon, 25 Nov 2024 07:18:33 GMT
• Title: Assessing the Answerability of Queries in Retrieval-Augmented Code Generation
• Authors: Geonmin Kim, Jaeyeon Kim, Hancheol Park, Wooksu Shin, Tae-Ho Kim,
• Abstract summary: This study proposes a task for evaluating answerability, which assesses whether valid answers can be generated. We build a benchmark dataset called Retrieval-augmented Code Generability Evaluation (RaCGEval) to evaluate the performance of models performing this task.
• Score: 7.68409881755304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utilized among software developers. While this has increased productivity, there are still frequent instances of incorrect codes being provided. In particular, there are cases where plausible yet incorrect codes are generated for queries from users that cannot be answered with the given queries and API descriptions. This study proposes a task for evaluating answerability, which assesses whether valid answers can be generated based on users' queries and retrieved APIs in RaCG. Additionally, we build a benchmark dataset called Retrieval-augmented Code Generability Evaluation (RaCGEval) to evaluate the performance of models performing this task. Experimental results show that this task remains at a very challenging level, with baseline models exhibiting a low performance of 46.7%. Furthermore, this study discusses methods that could significantly improve performance.

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