Summary of Quantitative Certification Of Bias in Large Language Models, by Isha Chaudhary et al.
Quantitative Certification of Bias in Large Language Models
by Isha Chaudhary, Qian Hu, Manoj Kumar, Morteza Ziyadi, Rahul Gupta, Gagandeep Singh
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel framework, QuaCer-B, is proposed to evaluate Large Language Models (LLMs) for bias in response to various demographic groups. The approach certifies LLMs by providing high-confidence bounds on the probability of unbiased responses given a distribution of prompts. The framework is demonstrated on several distributions of prompts, including those generated from random token sequences and manual jailbreaks. Results show that even top-performing LLMs are vulnerable to biases over certain prompt distributions, highlighting the need for certification frameworks like QuaCer-B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can give biased answers that hurt people’s representation. Right now, there aren’t enough ways to thoroughly check if these models are biased because they can’t handle a lot of inputs and don’t provide any guarantees. A new framework called QuaCer-B is designed to solve this problem. It gives a “certificate” that shows how likely an LLM is to give unbiased answers for different groups of people, based on a set of prompts. This helps us understand if an LLM is biased or not. The framework works by looking at how different sets of prompts affect the LLM’s responses. By using this framework, we can see that even the best-performing LLMs have biases when given certain types of prompts. |
Keywords
» Artificial intelligence » Probability » Prompt » Token
