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Large Language Models (LLMs) have been demonstrated effective for codegeneration. Due to the complexity and opacity of LLMs, little is known abouthow these models generate code. To deepen our understanding, we investigatewhether LLMs attend to the same parts of a natural language description ashuman programmers during code generation. An analysis of five LLMs on a popularbenchmark, HumanEval, revealed a consistent misalignment between LLMs' andprogrammers' attention. Furthermore, we found that there is no correlationbetween the code generation accuracy of LLMs and their alignment with humanprogrammers. Through a quantitative experiment and a user study, we confirmedthat, among twelve different attention computation methods, attention computedby the perturbation-based method is most aligned with human attention and isconstantly favored by human programmers. Our findings highlight the need forhuman-aligned LLMs for better interpretability and programmer trust.

Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation