  Large Language Models (LLMs) have impressive capabilities, but are also prone
to outputting falsehoods. Recent work has developed techniques for inferring
whether a LLM is telling the truth by training probes on the LLM's internal
activations. However, this line of work is controversial, with some authors
pointing out failures of these probes to generalize in basic ways, among other
conceptual issues. In this work, we curate high-quality datasets of true/false
statements and use them to study in detail the structure of LLM representations
of truth, drawing on three lines of evidence: 1. Visualizations of LLM
true/false statement representations, which reveal clear linear structure. 2.
Transfer experiments in which probes trained on one dataset generalize to
different datasets. 3. Causal evidence obtained by surgically intervening in a
LLM's forward pass, causing it to treat false statements as true and vice
versa. Overall, we present evidence that language models linearly represent the
truth or falsehood of factual statements. We also introduce a novel technique,
mass-mean probing, which generalizes better and is more causally implicated in
model outputs than other probing techniques.
