Summary of Mars: Meaning-aware Response Scoring For Uncertainty Estimation in Generative Llms, by Yavuz Faruk Bakman et al.
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
by Yavuz Faruk Bakman, Duygu Nur Yaldiz, Baturalp Buyukates, Chenyang Tao, Dimitrios Dimitriadis, Salman Avestimehr
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposed Meaning-Aware Response Scoring (MARS) method improves uncertainty estimation in generative large language models, enhancing reliability for high-stakes applications. By considering the semantic contribution of each token in generated sequences, MARS outperforms length-normalized scoring methods across five pre-trained LLMs on three closed-book question-answering datasets. The approach is validated on a Medical QA dataset, demonstrating its efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative large language models are super smart computers that can write and answer questions. However, they sometimes produce wrong answers, which can be a problem in important situations. To fix this, researchers are trying to figure out how accurate these models really are. One way to do this is by using something called uncertainty estimation. This paper introduces a new method for doing this, called Meaning-Aware Response Scoring (MARS). It looks at each word generated by the model and tries to understand what it means in the context of the question. This helps the model be more accurate. The researchers tested MARS on several datasets and found that it works really well. |
Keywords
* Artificial intelligence * Question answering * Token
