Summary of Multi-perspective Consistency Enhances Confidence Estimation in Large Language Models, by Pei Wang et al.
Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models
by Pei Wang, Yejie Wang, Muxi Diao, Keqing He, Guanting Dong, Weiran Xu
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel approach to improving confidence estimation in large language models (LLMs). The existing methods often struggle with overconfidence on incorrect answers, which can lead to inaccurate predictions. To address this issue, the authors introduce a Multi-Perspective Consistency (MPC) method that leverages complementary insights from different perspectives within and across models. The proposed MPC-Internal and MPC-Across approaches aim to mitigate the problem of overconfidence by considering multiple viewpoints. Experimental results on eight publicly available datasets demonstrate that the MPC achieves state-of-the-art performance, and further analyses suggest its scalability to other models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure big language models are not too confident in their wrong answers. Right now, many methods have this problem. The researchers came up with a new way to fix it called Multi-Perspective Consistency (MPC). They used different views inside the model and even compared it to other models to make it more accurate. This worked really well on lots of datasets and is good for big language models too. |
