claim

active

claim:truth-directions-emerge-in-earlier-layers-for-factual-tasks-and-later-layers-for-arithmetic-tasks

Truth directions emerge in earlier layers for factual tasks and later layers for arithmetic tasks.

Core empirical claim about the layer-dependence of truth direction emergence as a function of task type.

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
supports

Findings (1)

finding

Factual tasks F0-F3 reach near-perfect AUROC in early-to-mid layers of Llama-3.1-8B; arithmetic tasks A1-A3 emerge much later; counting tasks F4-F5 emerge late similar to arithmetic.
supports
Core empirical finding about layer-dependent truth direction emergence across task types.

Hypotheses (1)

hypothesis

We hypothesize that LLMs represent correctness of arithmetic expressions differently from factual statements.
supports
Core working hypothesis motivating the factual vs. arithmetic task split in the experimental design.

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.

No single layer is universally optimal for probing truth directions; different tasks peak at different layers.claim0.843
Argues against the single-layer analysis approach of prior work.
Truth directions fail to generalize to harder tasks (F3-F5) regardless of prompt template because activations remain highly entangled.claim0.835
Establishes task difficulty as a hard limit that instructions cannot overcome.
The need for genuine counting over lists of more than two elements introduces the key limitation of truth directions.claim0.832
Identified as the exact computational operation that breaks truth direction generalization.
Truth-related directions reliably emerge at 60–75% of normalized layer depth in Qwen and Gemma modelsfinding0.828
Experiment 1 finding localizing where truth can be causally mediated
Reflection-inducing directions emerge more clearly in higher layers (ℓ>5) for both models and datasetsfinding0.815
Empirical observation about which network layers encode reflection-relevant information.
Single-layer analyses can be misleading because early-layer truth directions may reflect surface features with limited cross-task generalization.claim0.814
Methodological critique of prior work that fixed a single layer for truth probing.
We hypothesize that explicitly instructing the model to evaluate the correctness of the given statement may change the geometry of truth directions.hypothesis0.812
Motivating hypothesis for Section 5's investigation of prompt template effects.
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.claim0.807
Overarching conclusion summarizing the paper's contribution relative to prior universality claims.