CRCL at SemEval-2024 Task 2: Simple prompt optimizations
This work addresses a domain-specific challenge in clinical trial analysis, but it is incremental as it builds on existing prompt optimization methods.
The authors tackled the problem of determining inference relationships between clinical trial report sections and statements by applying prompt optimization techniques with LLM Instruct models, finding that synthetic Chain-of-Thought prompts significantly enhanced manually crafted ones.
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.