NaSh: Guardrails for an LLM-Powered Natural Language Shell
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915346251776000 |
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| author | Gyawali, Bimal Raj Achalla, Saikrishna Kallas, Konstantinos Kumar, Sam |
| author_facet | Gyawali, Bimal Raj Achalla, Saikrishna Kallas, Konstantinos Kumar, Sam |
| contents | We explore how a shell that uses an LLM to accept natural language input might be designed differently from the shells of today. As LLMs may produce unintended or unexplainable outputs, we argue that a natural language shell should provide guardrails that empower users to recover from such errors. We concretize some ideas for doing so by designing a new shell called NaSh, identify remaining open problems in this space, and discuss research directions to address them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NaSh: Guardrails for an LLM-Powered Natural Language Shell Gyawali, Bimal Raj Achalla, Saikrishna Kallas, Konstantinos Kumar, Sam Operating Systems Artificial Intelligence We explore how a shell that uses an LLM to accept natural language input might be designed differently from the shells of today. As LLMs may produce unintended or unexplainable outputs, we argue that a natural language shell should provide guardrails that empower users to recover from such errors. We concretize some ideas for doing so by designing a new shell called NaSh, identify remaining open problems in this space, and discuss research directions to address them. |
| title | NaSh: Guardrails for an LLM-Powered Natural Language Shell |
| topic | Operating Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2506.13028 |