LLMs Know More Than They Show: On the Intrinsic Representation of LLM
  Hallucinations
Authors: name: Hadas Orgad, name: Michael Toker, name: Zorik Gekhman, name: Roi Reichart, name: Idan Szpektor, name: Hadas Kotek, name: Yonatan Belinkov

Large language models (LLMs) often produce errors, including factual
inaccuracies, biases, and reasoning failures, collectively referred to as
"hallucinations". Recent studies have demonstrated that LLMs' internal states
encode information regarding the truthfulness of their outputs, and that this
information can be utilized to detect errors. In this work, we show that the
internal representations of LLMs encode much more information about
truthfulness than previously recognized. We first discover that the
truthfulness information is concentrated in specific tokens, and leveraging
this property significantly enhances error detection performance. Yet, we show
that such error detectors fail to generalize across datasets, implying that --
contrary to prior claims -- truthfulness encoding is not universal but rather
multifaceted. Next, we show that internal representations can also be used for
predicting the types of errors the model is likely to make, facilitating the
development of tailored mitigation strategies. Lastly, we reveal a discrepancy
between LLMs' internal encoding and external behavior: they may encode the
correct answer, yet consistently generate an incorrect one. Taken together,
these insights deepen our understanding of LLM errors from the model's internal
perspective, which can guide future research on enhancing error analysis and
mitigation.