Summary of Promoting Equality in Large Language Models: Identifying and Mitigating the Implicit Bias Based on Bayesian Theory, by Yongxin Deng (1) et al.
Promoting Equality in Large Language Models: Identifying and Mitigating the Implicit Bias based on Bayesian Theory
by Yongxin Deng, Xihe Qiu, Xiaoyu Tan, Jing Pan, Chen Jue, Zhijun Fang, Yinghui Xu, Wei Chu, Yuan Qi
First submitted to arxiv on: 20 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 framework, Bayesian-Theory based Bias Removal (BTBR), aims to address the issue of implicit biases in large language models (LLMs) trained on extensive text corpora. Existing prompt-based attack methods can still extract biases from the model’s weights despite techniques like Affective Alignment. The authors formally define the problem and develop a novel approach to remove biases using Bayesian theory. By employing likelihood ratio screening, BTBR identifies biased data entries in publicly accessible datasets, constructs relevant knowledge triples, and expunges bias information from LLMs through model editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are trained on huge amounts of text data, which can include biased information. This bias can be hidden when the model is asked to do the same task for different groups of people. To fix this problem, researchers developed a new method called Bayesian-Theory based Bias Removal (BTBR). BTBR helps identify and remove biases from LLMs by looking at how likely certain data points are to contain biased information. |
Keywords
» Artificial intelligence » Alignment » Likelihood » Prompt
