Evaluating AI for Law: Bridging the Gap with Open-Source Solutions
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911845217992704 |
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| author | Bhambhoria, Rohan Dahan, Samuel Li, Jonathan Zhu, Xiaodan |
| author_facet | Bhambhoria, Rohan Dahan, Samuel Li, Jonathan Zhu, Xiaodan |
| contents | This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific knowledge to overcome these issues. The paper advocates for creating open-source legal AI systems to improve accuracy, transparency, and narrative diversity, addressing general AI's shortcomings in legal contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating AI for Law: Bridging the Gap with Open-Source Solutions Bhambhoria, Rohan Dahan, Samuel Li, Jonathan Zhu, Xiaodan Artificial Intelligence Human-Computer Interaction This study evaluates the performance of general-purpose AI, like ChatGPT, in legal question-answering tasks, highlighting significant risks to legal professionals and clients. It suggests leveraging foundational models enhanced by domain-specific knowledge to overcome these issues. The paper advocates for creating open-source legal AI systems to improve accuracy, transparency, and narrative diversity, addressing general AI's shortcomings in legal contexts. |
| title | Evaluating AI for Law: Bridging the Gap with Open-Source Solutions |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2404.12349 |