Getting More out of Large Language Models for Proofs
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| Publicado en: | arXiv.org (May 31, 2023), p. n/a |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Cornell University Library, arXiv.org
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2811359378 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2811359378 | ||
| 045 | 0 | |b d20230531 | |
| 100 | 1 | |a Zhang, Shizhuo Dylan | |
| 245 | 1 | |a Getting More out of Large Language Models for Proofs | |
| 260 | |b Cornell University Library, arXiv.org |c May 31, 2023 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Large language models have the potential to simplify formal theorem proving and make it more accessible. But how to get the most out of these models is still an open question. To answer this question, we take a step back and explore the failure cases of these models using common prompting-based techniques. Our talk will discuss these failure cases and what they can teach us about how to get more out of these models. | |
| 653 | |a Questions | ||
| 653 | |a Large language models | ||
| 700 | 1 | |a Ringer, Talia | |
| 700 | 1 | |a First, Emily | |
| 773 | 0 | |t arXiv.org |g (May 31, 2023), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2811359378/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2305.04369 |