The field of Large Language Models (LLMs) is moving towards a more nuanced understanding of value alignment, with a growing recognition of the importance of pluralistic and contextual considerations. Recent research has highlighted the limitations of current value probing strategies, which are vulnerable to perturbations and may not capture the complexity of human values. There is a growing need for more advanced methods that can navigate multiple values and reconcile conflicting demands. Innovations in in-context learning and value alignment are showing promise in addressing these challenges. Notably, some studies have introduced novel methods for optimizing value instructions and improving the alignment of LLMs with human moral judgments. Noteworthy papers include: PICACO, which proposes a novel pluralistic in-context value alignment method that optimizes a meta-instruction to better elicit LLMs' understanding of multiple values. The Pluralistic Moral Gap, which introduces a benchmark dataset and a Dirichlet-based sampling method to improve the alignment of LLMs with human moral judgments and enhance value diversity.