FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in Biomedical Domain
<|reference_start|>FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in Biomedical Domain: 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.<|reference_end|>
@article{liu2024fzi-wim,
    title={FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in
  Biomedical Domain},
    author={Jin Liu and Steffen Thoma},
    journal={The 18th International Workshop on Semantic Evaluation
  (SemEval-2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10040},
    primaryClass={cs.CL cs.AI}
}