Software Engineering Methods For AI-Driven Deductive Legal Reasoning
সংরক্ষণ করুন:
| প্রকাশিত: | arXiv.org (Jun 27, 2024), p. n/a |
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| প্রধান লেখক: | |
| প্রকাশিত: |
Cornell University Library, arXiv.org
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full text outside of ProQuest |
| ট্যাগগুলো: |
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| 001 | 3039629860 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3039629860 | ||
| 045 | 0 | |b d20240627 | |
| 100 | 1 | |a Padhye, Rohan | |
| 245 | 1 | |a Software Engineering Methods For AI-Driven Deductive Legal Reasoning | |
| 260 | |b Cornell University Library, arXiv.org |c Jun 27, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a The recent proliferation of generative artificial intelligence (AI) technologies such as pre-trained large language models (LLMs) has opened up new frontiers in computational law. An exciting area of development is the use of AI to automate the deductive rule-based reasoning inherent in statutory and contract law. This paper argues that such automated deductive legal reasoning can now be viewed from the lens of software engineering, treating LLMs as interpreters of natural-language programs with natural-language inputs. We show how it is possible to apply principled software engineering techniques to enhance AI-driven legal reasoning of complex statutes and to unlock new applications in automated meta-reasoning such as mutation-guided example generation and metamorphic property-based testing. | |
| 653 | |a Mathematical logic | ||
| 653 | |a Large language models | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Documents | ||
| 653 | |a Reasoning | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Contract law | ||
| 653 | |a Program verification (computers) | ||
| 653 | |a Software engineering | ||
| 653 | |a Automation | ||
| 653 | |a Natural language processing | ||
| 653 | |a Natural language | ||
| 653 | |a Software testing | ||
| 773 | 0 | |t arXiv.org |g (Jun 27, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3039629860/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2404.09868 |