Summary of Fzi-wim at Semeval-2024 Task 2: Self-consistent Cot For Complex Nli in Biomedical Domain, by Jin Liu and Steffen Thoma
FZI-WIM at SemEval-2024 Task 2: Self-Consistent CoT for Complex NLI in Biomedical Domain
by Jin Liu, Steffen Thoma
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel inference system for the SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials, which employs the chain of thought (CoT) paradigm to tackle complex reasoning problems. The system improves CoT performance through self-consistency and utilizes majority voting for final verification instead of greedy decoding. The self-consistent CoT system achieves a baseline F1 score of 0.80, faithfulness score of 0.90, and consistency score of 0.73. The paper releases its code and data publicly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a complex problem in natural language processing called biomedical inference for clinical trials. It uses a new way to think about reasoning called chain of thought (CoT) and makes it better by making sure it’s consistent with itself. Instead of always choosing the first answer, it looks at many possibilities and chooses the most popular one. The system does well on some important measures: 80% for being correct overall, 90% for being faithful to the original text, and 73% for being consistent. You can see how they did it and use their code and data. |
Keywords
» Artificial intelligence » F1 score » Inference » Natural language processing
