Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
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Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.
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A Study of LLMs' Preferences for Libraries and Programming Languages
Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.
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Same Scrutiny, More Time: Eye Tracking Insights into Reviewing LLM-Labelled Code
Eye-tracking experiment finds that labeling code as LLM-generated increases fixation time without changing review thoroughness, with reviewers adapting criteria or using the prompt.