{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/crcl-at-semeval-2024-task-2-simple-prompt","title":"CRCL at SemEval-2024 Task 2: Simple prompt optimizations","arxiv_id":"2405.01942","date":"2024-05-03","proceeding":null,"authors":["Clément Brutti-Mairesse","Loïc Verlingue"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2405.01942v1","url_pdf":"https://arxiv.org/pdf/2405.01942v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"crcl-at-semeval-2024-task-2-simple-prompt","repo_url":"https://github.com/clementbm-clb/semeval-2024","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}