A recent study led by the Technion-Israel Institute of Technology reveals that large language models (LLMs) may be more knowledgeable than they appear. Researchers found that LLMs' internal representations encode information about the correctness of their outputs, allowing them to identify the correct answer even when they ultimately generate an incorrect one. The research team focused on analyzing LLM errors in long text generation, which is more aligned with their real-world application scenarios. They constructed an error detection dataset to compare the model's outputs.

Recently, a study published in Nature revealed a thought-provoking phenomenon in the development of artificial intelligence: as large language models (LLMs) continue to evolve, they increasingly tend to provide answers with 'full confidence', even when those answers may be incorrect. This phenomenon has sparked broad discussions on AI reliability and the risks of use. The research team, led by José Hernández-Orallo from the Valencia Institute of Artificial Intelligence in Spain, conducted an in-depth analysis of AI models.