Summary of Causal-guided Active Learning For Debiasing Large Language Models, by Li Du et al.
Causal-Guided Active Learning for Debiasing Large Language Models
by Li Du, Zhouhao Sun, Xiao Ding, Yixuan Ma, Yang Zhao, Kaitao Qiu, Ting Liu, Bing Qin
First submitted to arxiv on: 23 Aug 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 Casual-Guided Active Learning (CAL) framework aims to address the issue of generative Large Language Models (LLMs) capturing dataset biases and utilizing them for generation, leading to poor generalizability and harmfulness. By combining active learning with causal mechanisms, CAL utilizes LLMs itself to automatically identify informative biased samples and induce bias patterns. A cost-effective in-context learning method is then employed to prevent LLMs from utilizing dataset biases during generation. Experimental results demonstrate the effectiveness of CAL in recognizing typical biased instances and inducing various bias patterns for debiasing LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to make Large Language Models (LLMs) better by teaching them not to rely on biases in the data they were trained on. Right now, these models can capture biases and use them to generate text that is harmful or unfair. The authors propose a method called Casual-Guided Active Learning (CAL) that helps LLMs learn from their own mistakes and avoid using biased information. |
Keywords
» Artificial intelligence » Active learning
