Summary of Biaskg: Adversarial Knowledge Graphs to Induce Bias in Large Language Models, by Chu Fei Luo et al.
BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models
by Chu Fei Luo, Ahmad Ghawanmeh, Xiaodan Zhu, Faiza Khan Khattak
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers propose a novel methodology to evaluate the effectiveness of mitigation strategies for large language models (LLMs) in eliminating social biases. They refactor natural language stereotypes into a knowledge graph and use adversarial attacking strategies to induce biased responses from various LLMs, including those trained with safety guardrails. The study finds that their approach increases bias in all models, highlighting the need for further research in AI safety. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have become incredibly powerful, but they can also be biased. This paper shows how to test if these biases are gone after trying to fix them. They take common stereotypes and turn them into a special kind of diagram called a knowledge graph. Then, they use clever tricks to make the language models produce biased answers. Surprisingly, even the best-trained models still show bias. This means we need to keep working on making AI safer. |
Keywords
» Artificial intelligence » Knowledge graph
