Summary of Relative Counterfactual Contrastive Learning For Mitigating Pretrained Stance Bias in Stance Detection, by Jiarui Zhang and Shaojuan Wu and Xiaowang Zhang and Zhiyong Feng
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection
by Jiarui Zhang, Shaojuan Wu, Xiaowang Zhang, Zhiyong Feng
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
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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 authors propose a novel approach to mitigating biased language models in stance detection, where they introduce Relative Counterfactual Contrastive Learning (RCCL) to overcome the difficulties of measuring bias. They develop a structural causal model to identify the relationships between context, pre-trained language models, and stance relations, and then generate target-aware relative stance samples using masked language model predictions. The proposed method leverages contrastive learning based on counterfactual theory to debias the language models while preserving context-specific stance relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make language models fairer when they’re used for understanding how people feel about certain topics. Right now, these models can be biased because of the information they were trained on. The authors want to fix this problem by making the model think about how it would respond differently if someone’s opinion was opposite of what it normally sees. They came up with a new way to do this using something called contrastive learning. This method helps the model understand that its initial answer might not be right and make a more accurate one instead. |
Keywords
» Artificial intelligence » Masked language model
