Summary of Self-debiasing Large Language Models: Zero-shot Recognition and Reduction Of Stereotypes, by Isabel O. Gallegos et al.
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
by Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 The paper proposes a novel approach to reducing harmful social biases in large language models (LLMs) without requiring access to the model’s training data or parameters. The method, called “zero-shot self-debiasing,” leverages the LLM’s ability to generate text based on prompts to identify and correct stereotypical assumptions. Two techniques are introduced: self-debiasing via explanation, which uses the LLM’s generated explanations to identify invalid assumptions, and self-debiasing via reprompting, which delivers more nuanced and less biased responses by rephrasing the input prompt. The paper demonstrates that these approaches can significantly reduce stereotyping across different social groups, relying only on the LLM itself and a simple prompt. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made great progress in understanding and generating language, but they also show harmful biases. To fix this, researchers have come up with many solutions, but most need access to special training data or model settings. This paper explores a new way to reduce bias without needing those things. They call it “zero-shot self-debiasing.” It works by using the LLM’s ability to generate text based on prompts to identify and correct stereotypical assumptions. Two methods are shown: one uses explanations generated by the LLM to spot invalid assumptions, while the other rephrases the prompt to get more nuanced responses that avoid bias. The paper demonstrates that these approaches can reduce stereotypes across different groups. |
Keywords
* Artificial intelligence * Prompt * Zero shot
