The field of large language models (LLMs) is moving towards improving alignment with human values and cultural competence. Recent research has focused on developing innovative methods to evaluate and enhance the trustworthiness and cultural awareness of LLMs. This includes the creation of benchmarks and fine-tuning frameworks to assess and improve value alignment across diverse populations and cultures. Notable papers in this area include: Ensemble Debates with Local Large Language Models for AI Alignment, which demonstrates the effectiveness of ensemble debates in improving alignment-oriented reasoning. Exploring and Mitigating Fawning Hallucinations in Large Language Models proposes a contrastive decoding method to mitigate fawning hallucinations and improve factuality. We Politely Insist: Your LLM Must Learn the Persian Art of Taarof introduces TaarofBench, a benchmark for evaluating LLM understanding of Persian taarof, and achieves significant improvements in cultural competence through supervised fine-tuning and Direct Preference Optimization. EigenBench: A Comparative Behavioral Measure of Value Alignment proposes a black-box method for comparatively benchmarking language models' values, and MVPBench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values introduces a novel benchmark for evaluating LLMs' alignment with multi-dimensional human value preferences across 75 countries.