New Evaluation Framework Shows Instruction-Following Matters More Than Functional Correctness for Code Generation
Researchers introduced SWE-IF, a new evaluation framework that measures both functional correctness and instruction-following in code generated by large language models, addressing the gap between what current metrics capture and what users actually prefer. The study evaluated 31 LLMs and found that instruction-following—whether code follows non-functional user preferences like readability and style—is the primary differentiator among models and correlates better with human preference than functional correctness alone. This work suggests that code evaluation metrics need to expand beyond pass@k benchmarks to capture the full spectrum of user expectations in AI-assisted coding.
Researchers at arXiv have published a study introducing SWE-IF, a new testbed for evaluating large language models on code generation tasks. The work addresses a gap in current evaluation practices: while existing metrics like pass@k measure only whether code works functionally, users often care about non-functional qualities such as readability, code style, and adherence to specific instructions. The researchers developed VeriCode, a taxonomy of 30 verifiable code instructions with deterministic verifiers, and used it to augment established evaluation suites. Testing 31 LLMs, they found that even the strongest models struggle to comply with multiple instructions simultaneously and sometimes exhibit functional regression when attempting to follow them. Critically, a composite score combining functional correctness and instruction-following correlated best with human preference, with instruction-following emerging as the primary differentiator among models. The authors have made their code, data, and taxonomy publicly available.
What's missing
The study's own limitations and scope constraints are not detailed in the abstract provided. Specific information about which 31 LLMs were evaluated, the composition of the human preference evaluation dataset, inter-rater agreement metrics for human judgments, and potential limitations of the deterministic verifiers in capturing subjective code quality aspects would strengthen understanding of the work's applicability.
What different sources said
- arXiv cs.AICenter
Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks
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