FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in Biomedical Domain
This work addresses the challenge of safe biomedical natural language inference for clinical trials, representing an incremental improvement over existing chain of thought methods.
The paper tackled the problem of complex natural language inference in the biomedical domain by applying a self-consistent chain of thought method, achieving a baseline F1 score of 0.80 (ranking 1st) and a faithfulness score of 0.90 (ranking 3rd).
This paper describes the inference system of FZI-WIM at the SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials. Our system utilizes the chain of thought (CoT) paradigm to tackle this complex reasoning problem and further improves the CoT performance with self-consistency. Instead of greedy decoding, we sample multiple reasoning chains with the same prompt and make the final verification with majority voting. The self-consistent CoT system achieves a baseline F1 score of 0.80 (1st), faithfulness score of 0.90 (3rd), and consistency score of 0.73 (12th). We release the code and data publicly https://github.com/jens5588/FZI-WIM-NLI4CT.