Summary of Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach, by Daiki Shirafuji et al.
Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach
by Daiki Shirafuji, Makoto Takenaka, Shinya Taguchi
First submitted to arxiv on: 16 Dec 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 “Bias Vector” method mitigates language model (LM) biases without requiring manual debiasing data. This approach involves continual training pre-trained LMs on biased data using masked language modeling, constructing the Bias Vector as the difference between LM weights, and subtracting it from pre-trained LM weights for debiasing. The method demonstrates an average improvement of 0.177 points on the SEAT across three LMs, with no degradation in performance on downstream tasks in the GLUE benchmark. This paper presents a comprehensive evaluation of debiased LMs across both the SEAT and GLUE benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models can be biased and what we can do to make them fairer. Language models are trained on lots of text data, but this data often has biases and stereotypes that get reflected in the model’s outputs. This is a problem because it can lead to unfair results or even perpetuate social problems. The researchers propose a new way to fix this called the “Bias Vector” method. It works by updating the language model’s weights based on how biased they are, which helps remove biases and makes the model more fair. The researchers tested this method and found that it works well without affecting the model’s overall performance. |
Keywords
» Artificial intelligence » Language model
