Summary of Decoding Biases: Automated Methods and Llm Judges For Gender Bias Detection in Language Models, by Shachi H Kumar et al.
Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models
by Shachi H Kumar, Saurav Sahay, Sahisnu Mazumder, Eda Okur, Ramesh Manuvinakurike, Nicole Beckage, Hsuan Su, Hung-yi Lee, Lama Nachman
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large Language Models (LLMs) excel at language understanding and text generation but are vulnerable to adversarial attacks, allowing malicious users to prompt undesirable text. Additionally, LLMs inherently encode biases that can lead to harmful effects during interactions. To address this, we propose training models to automatically generate adversarial prompts for target LLMs. We introduce LLM-based bias evaluation metrics and analyze existing automatic methods and metrics. Our approach assesses the strengths and weaknesses of model families and identifies where current evaluation methods fall short. Notably, we compare these metrics to human evaluation and find that the LLM-as-a-Judge metric aligns with human judgement on bias in response generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on big language models that can understand and generate human-like text. However, these models have a problem: they can be tricked into generating bad responses by malicious users. This is because the models have biases built-in that can cause harm. To solve this issue, scientists are developing new ways to evaluate the bias in these models’ responses. They’re using machine learning techniques to create prompts that will get the models to produce biased text. The goal is to develop better metrics for measuring bias and to understand how different models handle language tasks. |
Keywords
» Artificial intelligence » Language understanding » Machine learning » Prompt » Text generation
