Summary of Iitk at Semeval-2024 Task 2: Exploring the Capabilities Of Llms For Safe Biomedical Natural Language Inference For Clinical Trials, by Shreyasi Mandal and Ashutosh Modi
IITK at SemEval-2024 Task 2: Exploring the Capabilities of LLMs for Safe Biomedical Natural Language Inference for Clinical Trials
by Shreyasi Mandal, Ashutosh Modi
First submitted to arxiv on: 6 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research investigates the robustness and consistency of large language models (LLMs) when performing natural language inference (NLI) on breast cancer clinical trial reports. The study evaluates the reasoning capabilities of LLMs, including pre-trained language models GPT-3.5 and Gemini Pro, under zero-shot settings using the Retrieval-Augmented Generation framework. The results show that LLMs can achieve an F1 score of 0.69, consistency of 0.71, and faithfulness score of 0.90 on the test dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well big language models do when trying to understand what’s being said about breast cancer clinical trials. It wants to know if these models can figure out what’s important and ignore what’s not. The study compares different kinds of language models, like GPT-3.5 and Gemini Pro, to see how they do. The results show that the models can be pretty good at understanding what they’re reading. |
Keywords
» Artificial intelligence » F1 score » Gemini » Gpt » Inference » Retrieval augmented generation » Zero shot
