Summary of Mitigating Boundary Ambiguity and Inherent Bias For Text Classification in the Era Of Large Language Models, by Zhenyi Lu et al.
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models
by Zhenyi Lu, Jie Tian, Wei Wei, Xiaoye Qu, Yu Cheng, Wenfeng xie, Dangyang Chen
First submitted to arxiv on: 11 Jun 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 The proposed two-stage classification framework for large language models (LLMs) effectively mitigates the issues of ambiguous decision boundaries and inherent biases towards specific tokens and positions. By employing a self-reduction technique to narrow down options, followed by pairwise contrastive comparisons in a chain-of-thought manner, LLMs can achieve consistent improvements on various text classification tasks. The framework is evaluated on four datasets (Banking77, HWU64, LIU54, and Clinic150) and outperforms existing methods. The authors’ code and data are available for further exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at many things, but they can struggle with text classification tasks when faced with lots of options. This problem is called “ambiguous decision boundaries” and it’s a big issue because it makes the model choose wrong answers. To fix this, researchers have come up with a new way to make the model work better. It involves two steps: first, the model looks at all the options and narrows them down, then it compares each option to see which one is the best choice. This new method works really well on four different datasets and can even help other models do better. |
Keywords
» Artificial intelligence » Classification » Text classification
