The field of large language models (LLMs) is witnessing significant developments in addressing bias and improving their performance in simulating public opinions, analyzing climate policy, and evaluating geopolitical and cultural bias. Researchers are proposing new methods to evaluate and mitigate bias in LLMs, such as using human survey data as in-context examples, constructing manually curated datasets, and developing frameworks for evaluating LLM behavior across neutral and sensitive topics. Additionally, studies are investigating the ability of LLMs to answer complex policy questions, with promising results in analyzing climate policy documents. Noteworthy papers include: A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs, which offers a structured framework for evaluating LLM behavior. IMPACT: Inflectional Morphology Probes Across Complex Typologies, which introduces a synthetically generated evaluation framework to assess LLM performance in inflectional morphology. MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion, which presents a novel post-training alignment framework for LLMs.