Making LLMs more accurate by using all of their layers
Google Research has developed a new framework to evaluate the behavioral alignment of large language models with human social inclinations. This approach adapts established psychological questionnaires into large-scale situational judgment tests, allowing for the quantification of model tendencies in realistic scenarios. The research identifies gaps where model behaviors deviate from human consensus or fail to capture the range of human opinions, aiming to improve LLM navigation of social dynamics. Separately, Google Research also introduced SLED, a novel decoding strategy that enhances LLM factuality by utilizing all model layers instead of just the final one, without requiring external data or fine-tuning. AI
IMPACT New methods for evaluating LLM alignment and improving factuality could lead to more trustworthy and socially adept AI systems.