CRCL at SemEval-2024 Task 2: Simple prompt optimizations
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Abstract (arXiv)
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.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 319
- 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4cited by 202
- 2023Adapting Large Language Models to Domains via Reading Comprehensioncited by 48
- 2024SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trialscited by 20
- 2023NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reportscited by 18
- 2023Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal Perspectivescited by 5
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