Summary of On Mitigating Code Llm Hallucinations with Api Documentation, by Nihal Jain et al.
On Mitigating Code LLM Hallucinations with API Documentation
by Nihal Jain, Robert Kwiatkowski, Baishakhi Ray, Murali Krishna Ramanathan, Varun Kumar
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study tackles the issue of API hallucinations in software engineering contexts by introducing a new benchmark called CloudAPIBench. The researchers use this tool to measure API hallucination occurrences and provide annotations for frequencies of API occurrences in the public domain, allowing them to study API hallucinations at various frequency levels. The results show that Code LLMs struggle with low-frequency APIs, achieving only 38.58% valid low-frequency API invocations with GPT-4o. However, by using Documentation Augmented Generation (DAG), the performance improves for low-frequency APIs (47.94%), but negatively impacts high-frequency APIs when using sub-optimal retrievers. To mitigate this, the authors propose intelligently triggering DAG by checking against an API index or leveraging Code LLMs’ confidence scores to retrieve only when needed. The proposed methods enhance the balance between low and high frequency API performance, resulting in more reliable API invocations (8.20% absolute improvement on CloudAPIBench for GPT-4o). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a new tool called CloudAPIBench to measure how often APIs are misused or invented in software engineering projects. They found that some AI models struggle with using low-frequency APIs correctly and introduced a method called Documentation Augmented Generation (DAG) to help these models perform better. The results show that DAG works well for low-frequency APIs, but not as well for high-frequency ones. To solve this problem, the authors suggest making DAG work more intelligently by checking against an API index or using the AI model’s confidence level before retrieving information. |
Keywords
» Artificial intelligence » Gpt » Hallucination
