CRCL at SemEval-2024 Task 2: Simple prompt optimizations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913340100444160 |
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| author | Brutti-Mairesse, Clément Verlingue, Loïc |
| author_facet | Brutti-Mairesse, Clément Verlingue, Loïc |
| contents | We present a baseline for the SemEval 2024 task 2 challenge, whose objective is to ascertain the inference relationship between pairs of clinical trial report sections and statements. We apply prompt optimization techniques with LLM Instruct models provided as a Language Model-as-a-Service (LMaaS). We observed, in line with recent findings, that synthetic CoT prompts significantly enhance manually crafted ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CRCL at SemEval-2024 Task 2: Simple prompt optimizations Brutti-Mairesse, Clément Verlingue, Loïc Computation and Language We present a baseline for the SemEval 2024 task 2 challenge, whose objective is to ascertain the inference relationship between pairs of clinical trial report sections and statements. We apply prompt optimization techniques with LLM Instruct models provided as a Language Model-as-a-Service (LMaaS). We observed, in line with recent findings, that synthetic CoT prompts significantly enhance manually crafted ones. |
| title | CRCL at SemEval-2024 Task 2: Simple prompt optimizations |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2405.01942 |