LLM-Supported Natural Language to Bash Translation
This work addresses the challenge of accurately assessing LLM performance in Bash command translation for users who need to automate or simplify command-line interactions, though it is incremental as it builds on existing datasets and heuristics.
The authors tackled the problem of evaluating large language models (LLMs) for translating natural language to Bash commands by creating a manually verified test dataset of 600 pairs and a training dataset of 40,939 pairs, and introducing a functional equivalence heuristic that achieves 95% confidence, a 16% improvement over prior methods. Their evaluation shows that techniques like parsing and constrained decoding can boost NL2SH accuracy by up to 32%.
The Bourne-Again Shell (Bash) command-line interface for Linux systems has complex syntax and requires extensive specialized knowledge. Using the natural language to Bash command (NL2SH) translation capabilities of large language models (LLMs) for command composition circumvents these issues. However, the NL2SH performance of LLMs is difficult to assess due to inaccurate test data and unreliable heuristics for determining the functional equivalence of Bash commands. We present a manually verified test dataset of 600 instruction-command pairs and a training dataset of 40,939 pairs, increasing the size of previous datasets by 441% and 135%, respectively. Further, we present a novel functional equivalence heuristic that combines command execution with LLM evaluation of command outputs. Our heuristic can determine the functional equivalence of two Bash commands with 95% confidence, a 16% increase over previous heuristics. Evaluation of popular LLMs using our test dataset and heuristic demonstrates that parsing, in-context learning, in-weight learning, and constrained decoding can improve NL2SH accuracy by up to 32%. Our findings emphasize the importance of dataset quality, execution-based evaluation and translation method for advancing NL2SH translation. Our code is available at https://github.com/westenfelder/NL2SH