Summary of Eliciting Uncertainty in Chain-of-thought to Mitigate Bias Against Forecasting Harmful User Behaviors, by Anthony Sicilia and Malihe Alikhani
Eliciting Uncertainty in Chain-of-Thought to Mitigate Bias against Forecasting Harmful User Behaviors
by Anthony Sicilia, Malihe Alikhani
First submitted to arxiv on: 17 Oct 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 This paper investigates the potential biases of large language models (LLMs) in conversation forecasting tasks, specifically in social media moderation. The goal is to predict harmful user behaviors before they occur and enable preventative interventions. While LLMs have shown promise in this area, their biases are unclear, particularly when predicting outcomes that may be undesirable. The study explores the use of model uncertainty as a tool to mitigate these biases. Researchers ask three primary questions: (1) how does forecasting accuracy change when models represent their uncertainty; (2) how does bias change when models represent their uncertainty; and (3) how can uncertainty representations reduce or eliminate biases without requiring extensive training data. The study uses 5 open-source language models tested on 2 datasets designed to evaluate conversation forecasting for social media moderation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a system that can predict what might happen in an online conversation before it actually happens. This could help prevent harmful behavior, like cyberbullying. Large computers called language models are being used for this task, but we don’t know if they have biases that could make them less effective or even worse. This paper explores how these language models work and whether they can be made more fair by showing their uncertainty about the outcome of a conversation. The researchers test 5 different computer models on two special datasets to see how well they do and if they are biased. |
