Summary of Embedding Trajectory For Out-of-distribution Detection in Mathematical Reasoning, by Yiming Wang et al.
Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
by Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Zhuosheng Zhang, Rui Wang
First submitted to arxiv on: 22 May 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 proposed TV score method leverages trajectory volatility for out-of-distribution (OOD) detection in generative language models (GLMs), particularly in complex tasks like mathematical reasoning. Building on the effectiveness of embedding distance measurement methods in traditional linguistic tasks, this approach adapts to the high-density feature of output spaces characteristic of mathematical reasoning scenarios. Experimental results demonstrate that TV score outperforms traditional algorithms for GLMs under these conditions and has potential applications in other domains with similar output space features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, researchers are working on ways to keep deep networks safe from fake data by detecting when the input is unusual. This paper focuses on a specific type of language model that can generate text and uses a new method to detect when this generated text is out of the ordinary. The approach works well in tasks like math problem-solving, which requires complex reasoning, and can be applied to other areas where there’s a lot of information in the output. |
Keywords
» Artificial intelligence » Embedding » Language model
