LLMs possess the internal machinery to recognize knowledge gaps and adjust specificity accordingly, but their generation process doesn't use these signals—a gap that could be fixed through better training objectives.
Large language models often make up specific details about unfamiliar entities instead of admitting uncertainty. This paper shows that LLMs actually have internal signals detecting when they don't know something and can anticipate how specific their answer should be—but they ignore these signals during generation, preferring to sound confident anyway.