The recent developments in the research area of large language models (LLMs) and their applications in information access, argumentation, and bias mitigation are pushing the boundaries of AI-assisted discourse and decision-making. A significant trend is the use of LLMs to generate diverse viewpoints and simulate multi-persona debates, which has shown promise in reducing confirmation bias and enhancing information diversity. This approach not only fosters creative interactions but also promotes exposure to varied perspectives, potentially mitigating bias in information seeking. Another notable advancement is the integration of LLMs with genetic algorithms and adversarial search in debate platforms, which adaptively generate contextually relevant arguments, enhancing both educational and public discourse contexts. Additionally, there is a growing focus on evaluating and mitigating cognitive biases within LLMs, such as anchoring bias, through experimental studies and the development of comprehensive mitigation strategies. These efforts are crucial for ensuring the responsible deployment of LLMs in persuasive and decision-making contexts. Furthermore, the use of LLMs in assessing the impact of conspiracy theories and prioritizing claims for fact-checking highlights their potential in addressing societal challenges, although challenges related to bias and accuracy remain. Overall, the field is advancing towards more nuanced, evidence-based, and ethically sound applications of LLMs, with a strong emphasis on improving public discourse and decision-making processes.