Pith. sign in

REVIEW 6 cited by

Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.15098 v2 pith:K4BRHDHC submitted 2023-09-26 cs.CL cs.AIcs.LG

Mert Yuksekgonul , Varun Chandrasekaran , Erik Jones , Suriya Gunasekar , Ranjita Naik , Hamid Palangi , Ece Kamar , Besmira Nushi This is my paper
classification cs.CLcs.AIcs.LG
keywords factualattentionconstrainterrorsinvestigatelanguagellmsmodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constraint satisfaction problems and use this framework to investigate how the LLM interacts internally with factual constraints. We find a strong positive relationship between the LLM's attention to constraint tokens and the factual accuracy of generations. We curate a suite of 10 datasets containing over 40,000 prompts to study the task of predicting factual errors with the Llama-2 family across all scales (7B, 13B, 70B). We propose SAT Probe, a method probing attention patterns, that can predict factual errors and fine-grained constraint satisfaction, and allow early error identification. The approach and findings take another step towards using the mechanistic understanding of LLMs to enhance their reliability.

Discussion (0). Sign in to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Single Direction of Truth: An Observer Model's Linear Residual Probe Exposes and Steers Contextual Hallucinations

    cs.LG 2025-07 conditional novelty 7.0 of 10

    A single linear direction in an observer model's residual stream detects contextual hallucinations, transfers across models and datasets, and causally steers generation hallucination rates.

  2. Rethinking Cross-lingual Gaps from a Statistical Viewpoint

    cs.CL 2025-10 conditional novelty 6.0 of 10

    Cross-lingual accuracy gaps in LLMs are dominated by higher response variance in target languages, not missing knowledge; ensembling and variance-reduction prompts shrink the gap.

  3. Neural Message-Passing on Attention Graphs for Hallucination Detection

    cs.LG 2025-09 conditional novelty 6.0 of 10

    CHARM trains graph neural networks on token-attention graphs built from LLM computational traces and outperforms prior hallucination detectors on five benchmarks at token and response level.

  4. Is Your Model Thinking or Just Stagnating? PUMA: Diagnosing Reasoning Pathology via Phase-Momentum Alignment

    cs.AI 2026-07 conditional novelty 5.0 of 10

    PUMA detects when a reasoning model's entropy drop aligns with hidden-state momentum, truncates at that point, and reports improved accuracy-efficiency on 1.5B-32B reasoning models.

  5. How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception

    cs.CL 2025-05 conditional novelty 4.0 of 10

    Entity popularity and entity co-occurrence in Wikipedia correlate with LLM QA accuracy, confidence, and calibration, and combining them with confidence improves answer-correctness prediction by 5.24% on average.

  6. From Hallucinations to Jailbreaks: Rethinking the Vulnerability of Large Foundation Models

    cs.CV 2025-05 reject novelty 3.0 of 10

    The paper argues hallucinations and jailbreaks share the same optimization dynamics and shows that defenses for one also reduce the other, but the theoretical support is largely circular.

Pith tools