Google Research has published a paper advocating for large language models (LLMs) to express uncertainty more effectively. Presented at EMNLP 2024, the paper highlights that current LLMs often fail to indicate when they lack confidence in their responses. Researchers Gal Yona, Roee Aharoni, and Mor Geva propose a framework called "faithful response uncertainty" to align a model's expressed confidence with its internal certainty. The study reveals that existing alignment techniques prioritize fluency and helpfulness, inadvertently encouraging models to provide confident answers even when uncertain. This misalignment can lead to misleading outputs, particularly in fields like AI-driven trading, where the confidence level of predictions can significantly impact decision-making. The research underscores the need for new alignment methods to ensure AI tools accurately convey their confidence levels, which is crucial for users relying on AI for financial analysis.