The field of large language models (LLMs) is moving towards addressing the critical issue of bias in these models. Researchers are working to identify and mitigate various types of biases, including language bias, agreeableness bias, and harm-aware bias. This is essential for ensuring the fairness and reliability of LLMs in high-impact domains such as clinical decision support, legal analysis, and education.

Noteworthy papers in this area include: Does LLM Focus on the Right Words, which proposes a novel fine-tuning paradigm to mitigate language bias in LLM-based recommenders. Beyond Consensus, which introduces an optimal minority-veto strategy and a regression-based framework to mitigate the agreeableness bias in LLM judge evaluations. HALF, which presents a deployment-aligned framework for assessing model bias in realistic applications and weighing outcomes by harm severity. Evaluating and Mitigating LLM-as-a-judge Bias in Communication Systems, which systematically investigates judgment biases in LLM-as-a-judge models and proposes mitigation strategies.