The field of natural language processing is moving towards a greater emphasis on mitigating biases in large language models. Recent studies have highlighted the importance of evaluating and addressing biases in these models, particularly in areas such as gender bias, nation-level bias, and political bias. Researchers are developing new frameworks and methods for detecting and mitigating these biases, including the use of multidimensional evaluation metrics and debiasing techniques. Noteworthy papers in this area include one that investigates gender bias dynamics in synthetically generated data and finds that contrastive augmentation can achieve significant downstream bias reduction. Another paper presents a debiasing framework that combines Retrieval-Augmented Generation with Reflexion-based self-reflection techniques to reduce nation-level bias in large language models.
Mitigating Biases in Large Language Models
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Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data
"As Eastern Powers, I will veto." : An Investigation of Nation-level Bias of Large Language Models in International Relations
Analysing Personal Attacks in U.S. Presidential Debates
Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms
A Comprehensive Study of Implicit and Explicit Biases in Large Language Models
Bridging Human and Model Perspectives: A Comparative Analysis of Political Bias Detection in News Media Using Large Language Models
Bias in, Bias out: Annotation Bias in Multilingual Large Language Models
Disagreement is Disappearing on U.S. Cable Debate Shows