hypothesis

active

hypothesis:we-hypothesize-that-explicitly-instructing-the-model-to-evaluate-the-correctness-of-the-given-statement-may-change-the-geometry-of-truth-directions

We hypothesize that explicitly instructing the model to evaluate the correctness of the given statement may change the geometry of truth directions.

Motivating hypothesis for Section 5's investigation of prompt template effects.

Source paper

extracted_from

Testing the Limits of Truth Directions in LLMs

(2026) · Angelos Poulis · Mark Crovella · Evimaria Terzi

Neighborhood — ranked by edge-count

Papers (1)

paper

Testing the Limits of Truth Directions in LLMs
introduces

Findings (2)

finding

Probes trained under different explicit instruction prompts (ask-correct, ask-t/f, ask-able, ask-arith) are highly aligned with each other in cosine similarity.
associated_with
Shows the passive vs. active divide is more important than the specific wording of instructions.
Under ask-correct, arithmetic tasks A1-A2 show gradual AUROC increase peaking only in final layers, unlike the sharp transition under no-prompt.
supports
Shows that explicit instructions delay the emergence of truth directions in arithmetic tasks.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Does instructing the model to assess correctness affect the geometry of truth directions?question0.911
One of the three guiding research questions of the paper.
What is the effect of model instructions on truth directions?question0.839
Research question motivating Section 5.
Multiple semantically adjacent truth directions make models more vulnerable to manipulations that shift outputs without obvious signs in the primary truth directionclaim0.833
Safety implication derived from multi-dimensional truth structure finding
Universality claims for truth directions are more limited than previously assumed, with significant differences observable for various model layers, task difficulties, task types, and prompt templates.claim0.821
Overarching conclusion summarizing the paper's contribution relative to prior universality claims.
Truth directions emerge in earlier layers for factual tasks and later layers for arithmetic tasks.claim0.812
Core empirical claim about the layer-dependence of truth direction emergence as a function of task type.
Accuracy does not vary linearly with latent reflection directions; instead it follows a more non-linear mapping that requires deeper theoretical treatment.claim0.807
Theoretical limitation identified by the authors distinguishing reflection from stylistic tasks.
Linear truth directions in LLMs are reliable primarily in factual recall cases and break down when truth assessment depends on computing and storing intermediate results.claim0.806
Central empirical conclusion of the paper about the fundamental limits of truth directions.
Where inside the LLM should we look for an accurate truth direction that will generalize the most across tasks?question0.806
One of the three guiding research questions of the paper.