Summary of Lidao: Towards Limited Interventions For Debiasing (large) Language Models, by Tianci Liu et al.
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
by Tianci Liu, Haoyu Wang, Shiyang Wang, Yu Cheng, Jing Gao
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 Large language models have achieved impressive results in natural language generation tasks, but they struggle with generating negative and harmful content that is biased against certain demographic groups, raising fairness concerns. Previous attempts at intervening the generation process have resulted in notable trade-offs between fairness and fluency. This paper conducts a formal study from an information-theoretic perspective to explore the extent to which fluency must be affected to achieve a desired level of fairness. The authors propose LIDAO, a general framework for debiasing large language models at a better fluency provably. They also robustify their method in adversarial scenarios where prompts may stimulate LLMs to generate texts with fairness issues. Experiments on three large language models demonstrate the superiority of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can create text, but sometimes they make biased and harmful statements that are unfair to certain groups. Researchers tried to fix this by removing attitude or demographic information from the generated text, but this made the text less good. This paper wants to know if we really need to sacrifice some quality of the text to achieve fairness. They propose a new way to make language models more fair without making them worse at generating text. They tested their method on three different language models and it worked better than previous methods. |
