hypothesis

active

hypothesis:we-hypothesize-that-llms-represent-correctness-of-arithmetic-expressions-differently-from-factual-statements

We hypothesize that LLMs represent correctness of arithmetic expressions differently from factual statements.

Core working hypothesis motivating the factual vs. arithmetic task split in the experimental design.

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 (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.
associated_with
Core empirical finding about layer-dependent truth direction emergence across task types.

Claims (1)

claim

Truth directions emerge in earlier layers for factual tasks and later layers for arithmetic tasks.
supports
Core empirical claim about the layer-dependence of truth direction emergence as a function of task type.

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.

LLMs linearly represent truth-relevant information beyond the plausibility of text, as evidenced by probes trained on likely performing poorly on anti-correlated datasetsclaim0.834
Establishes that the observed linear structure is not merely a representation of text probability
LLMs sometimes know statements are false but generate them anyway, motivating the need for techniques that inspect internal model state rather than outputs aloneclaim0.818
Motivating claim supported by the CAPTCHA example and Perez et al. (2022) findings
Automated interpretability using LLMs can usefully score feature specificity.claim0.809
Claude 3 Opus ratings aligned with human judgment of feature descriptions.
The paper does not claim that the LLM itself is the source of meaning and value, only that meaning can be discerned in its output.claim0.808
Clarification to avoid misinterpretation.
As LLMs scale, they develop increasingly general abstractions, with large models linearly representing abstract concepts like truth that capture shared properties of diverse inputsclaim0.798
Interpretive claim connecting scale to abstraction level in LLM representations
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.claim0.798
Central empirical conclusion of the paper about the fundamental limits of truth directions.
LLMs can predict their own responses more accurately than external observers, implying privileged internal knowledgefinding0.798
Binder et al. finding cited as evidence that LLMs possess introspective capacity analogous to mindfulness
The better an LLM is at language modeling, the more it aligns with vision models, and vice versa — linear relationship between language modeling score and vision-language alignmentfinding0.796
Core cross-modal empirical result: larger and better language models align better with vision models