Summary of Refine-lm: Mitigating Language Model Stereotypes Via Reinforcement Learning, by Rameez Qureshi et al.
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
by Rameez Qureshi, Naïm Es-Sebbani, Luis Galárraga, Yvette Graham, Miguel Couceiro, Zied Bouraoui
First submitted to arxiv on: 18 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 paper introduces REFINE-LM, a debiasing method that utilizes reinforcement learning to tackle various types of biases in large language models without requiring significant computational resources or human annotations. The method trains a simple model on top of the word probability distribution of a LM, enabling bias agnostic debiasing. Experimental results demonstrate that REFINE-LM effectively reduces stereotypical biases while preserving LM performance, generalizes across different contexts, and is computationally efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to fix biased language models has been developed! The problem with large language models is that they can learn bad habits from the data they’re trained on. For example, they might think men are better at certain things or that some countries are more important than others. This is a big deal because it means the model will make unfair judgments. Some scientists have tried to fix this by “preprocessing” the data or making changes to how the words are represented inside the computer. But these methods need a lot of work and can only fix certain types of biases. The new method, called REFINE-LM, uses a special way of training that doesn’t require much work or computers. It works well on lots of different models and helps reduce unfair biases. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
