# Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception

**Authors:** Luyang Lin, Lingzhi Wang, Jinsong Guo, Kam-Fai Wong  
**arXiv:** [2403.14896](https://arxiv.org/abs/2403.14896)  
**Date:** 2024-03-22  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2403.14896v1/

## Key points

- Study title: "Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception"
- Emphasis on detecting media bias and the role of Large Language Models (LLMs)
- Concerns about inherent biases within LLMs
- Examination of biases within LLM systems themselves, beyond traditional approaches
- Investigation of biases in tasks related to political bias prediction and text continuation
- Exploration of bias across various topics to understand nuanced variations
- Proposal of debiasing strategies such as prompt engineering and model fine-tuning
- Extensive analysis of bias tendencies across different LLMs
- Advancement in understanding LLM bias and implications for bias detection tasks
- Contribution towards developing more robust and equitable AI systems

## Summary

In their study titled "Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception," authors Luyang Lin, Lingzhi Wang, Jinsong Guo, and Kam-Fai Wong delve into the pervasive issue of misinformation and disinformation in social media. They emphasize the critical need for detecting media bias and highlight how Large Language Models (LLMs) have become essential tools for predicting bias. However, concerns about inherent biases within these models persist. The research goes beyond traditional approaches that focus solely on detecting bias in media content by examining biases present within the LLM systems themselves. Through meticulous analysis, the authors investigate whether LLMs demonstrate biases in tasks related to political bias prediction and text continuation. Additionally, they explore bias across a range of topics to uncover nuanced variations in how bias is expressed within the framework of LLMs. One significant contribution of this study is the proposal of debiasing strategies such as prompt engineering and model fine-tuning to mitigate biases within LLMs. By conducting extensive analyses of bias tendencies across different LLMs, the research sheds light on the broader landscape of bias propagation in language models. Ultimately, this work advances our understanding of LLM bias and offers crucial insights into its implications for bias detection tasks. It also sets the stage for developing more robust and equitable AI systems that are less susceptible to biased outcomes.