Summary of Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective, By Yuchen Wen et al.
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
by Yuchen Wen, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 rigorously evaluates large language models (LLMs) for implicit bias towards certain demographics using psychometric principles from cognitive and social psychology. The authors introduce three attack approaches: Disguise, Deception, and Teaching, which are applied to two benchmarks: a bilingual dataset with 2.7K instances covering four bias types, and BUMBLE, a larger benchmark with 12.7K instances spanning nine common bias types. The evaluation shows that the proposed methods can effectively elicit LLMs’ inner bias, outperforming competitive baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can spread harmful content without using explicit bad words. This paper checks if these models have biases against certain groups of people. Researchers developed three ways to trick the models into agreeing with biased ideas: disguise, deception, and teaching. They created two big datasets to test this: one in two languages with 2,700 examples covering four types of bias, and another with 12,700 examples covering nine common biases. The results show that these methods can make the models reveal their biases better than other ways. |
