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cs.SE2024
Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths
Naryeong Kim, Sungmin Kang, Gabin An +1
Large Language Models are increasingly used to build agents to perform more complex tasks. As LLMs perform more complicated reasoning through longer interactions, self-consistency,…
cs.SE2024
Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects
Louis Milliken, Sungmin Kang, Shin Yoo
Many works have recently proposed the use of Large Language Model (LLM) based agents for performing `repository level' tasks, loosely defined as a set of tasks whose scopes are gre…
cs.SE2024
Identifying Inaccurate Descriptions in LLM-generated Code Comments via Test Execution
Sungmin Kang, Louis Milliken, Shin Yoo
Software comments are critical for human understanding of software, and as such many comment generation techniques have been proposed. However, we find that a systematic evaluation…