Hacker News: SWE-Bench tainted by answer leakage; real pass rates significantly lower

Feb 21, 2025

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Source URL: https://arxiv.org/abs/2410.06992
Source: Hacker News
Title: SWE-Bench tainted by answer leakage; real pass rates significantly lower

Feedly Summary: Comments

AI Summary and Description: Yes

Summary: The paper “SWE-Bench+: Enhanced Coding Benchmark for LLMs” addresses significant data quality issues in the evaluation of Large Language Models (LLMs) for coding tasks. It presents empirical analysis revealing problems like solution leakage and inadequate test cases, raising concerns about the accuracy of LLMs in software engineering contexts.

Detailed Description: The research centers around the SWE-bench dataset, initially introduced to evaluate the performance of LLMs in real-world software engineering scenarios. The paper identifies critical issues affecting the reliability of this dataset, which are vital for professionals in AI, especially those involved in LLM security and compliance.

– **Background**: SWE-bench is a dataset containing real-world GitHub issues and pull requests from Python projects, aimed to benchmark LLMs in coding contexts.
– **Key Findings**:
– **Solution Leakage**: Approximately 32.67% of successful code patches derived from LLMs like SWE-Agent + GPT-4 were based on solutions that were either provided in the issue itself or comments, undermining the integrity of the evaluation.
– **Weak Test Cases**: Around 31.08% of the accepted patches were flagged as suspicious due to inadequate testing mechanisms, which failed to ensure the correctness of the patches.
– **Significant Drop in Performance**: Upon excluding problematic instances, SWE-Agent + GPT-4’s resolution rate plummeted from 12.47% to 3.97%, highlighting the importance of data quality for accurate benchmarking.
– **Data Leakage Concerns**: Over 94% of the issues considered were created prior to LLMs’ knowledge cut-off dates, raising implications about the authenticity of the training data and potential privacy issues.

The insights from this analysis underline the necessity for rigorous data quality controls and validation processes in the development and evaluation of LLMs in cloud and AI security applications. Enhanced scrutiny can help in building more robust, secure, and reliable AI-driven coding assistants.

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