Summary of Prompting Fairness: Integrating Causality to Debias Large Language Models, by Jingling Li et al.
Prompting Fairness: Integrating Causality to Debias Large Language Models
by Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes a causality-guided debiasing framework to reduce social biases in large language models (LLMs). Despite their remarkable capabilities, LLMs are prone to generating biased responses, which can have significant implications for high-stakes decision-making. The proposed framework identifies how social information affects an LLM’s decision through different causal pathways and outlines principled prompting strategies to regulate these pathways. This framework unifies existing prompting-based debiasing techniques and opens up new directions for reducing bias by encouraging fact-based reasoning over reliance on biased social cues. Experiments on real-world datasets across multiple domains demonstrate the effectiveness of this framework in debiasing LLM decisions, even with black-box access to the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about big language models that sometimes say things that are unfair or discriminatory because they learned from biased information. This is a problem because these models can make important decisions for us, like who gets hired or what medical treatment someone should get. The researchers came up with a new way to fix this called the “causality-guided debiasing framework”. It helps identify how biased information affects the model’s decision and then shows how to fix it by giving the model better prompts. They tested this method on real-world data and showed that it can make language models less biased. |
Keywords
* Artificial intelligence * Prompting
