Summary of Cotar: Chain-of-thought Attribution Reasoning with Multi-level Granularity, by Moshe Berchansky et al.
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
by Moshe Berchansky, Daniel Fleischer, Moshe Wasserblat, Peter Izsak
First submitted to arxiv on: 16 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 The paper proposes an innovative approach to enhancing the performance of Large Language Models (LLMs) in question-answering tasks. By incorporating attributions from the input to the output, the method aims to reduce hallucinations and improve response accuracy. The authors introduce a Chain-of-Thought reasoning technique that focuses on generating attribution-centric outputs. Evaluations on two datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. Furthermore, combining this approach with finetuning enhances the performance of smaller LLMs, potentially outperforming GPT-4 in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve how computers answer questions by making them better at explaining why they gave a certain answer. This is important because computers currently just make things up sometimes! The new method focuses on giving explanations and makes sure they’re accurate. It tests this approach on two big question-answering datasets and shows it works really well. |
Keywords
» Artificial intelligence » Gpt » Question answering
