Pith. sign in

REVIEW 5 cited by

SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence

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 2502.08767 v2 pith:2NV53PTJ submitted 2025-02-12 cs.CL cs.AI

Zhining Liu , Rana Ali Amjad , Ravinarayana Adkathimar , Tianxin Wei , Hanghang Tong This is my paper
classification cs.CLcs.AI
keywords evidenceselfelicitcontextlanguagerelevantresponsesabilityaccurate
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Providing Language Models (LMs) with relevant evidence in the context (either via retrieval or user-provided) can significantly improve their ability to provide better-grounded responses. However, recent studies have found that LMs often struggle to fully comprehend and utilize key evidence from the context, especially when it contains noise and irrelevant information, an issue common in real-world scenarios. To address this, we propose SelfElicit, an inference-time approach that helps LMs focus on key contextual evidence through self-guided explicit highlighting. By leveraging the inherent evidence-finding capabilities of LMs using the attention scores of deeper layers, our method automatically identifies and emphasizes key evidence within the input context, facilitating more accurate and grounded responses without additional training or iterative prompting. We demonstrate that SelfElicit brings consistent and significant improvement on multiple evidence-based QA tasks for various LM families while maintaining computational efficiency. Our code and documentation are available at https://github.com/ZhiningLiu1998/SelfElicit.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

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

  1. HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling

    cs.CV 2025-09 unverdicted novelty 7.0 of 10

    HiDe is a training-free hierarchical decoupling method that separates key visual tokens from background interference in high-resolution MLLMs to achieve new state-of-the-art results on V*Bench, HRBench4K, and HRBench8K.

  2. On Improving Faithfulness of Podcasts from Documents

    cs.CL 2026-07 conditional novelty 6.0 of 10

    AI-generated podcasts often add unsupported claims; a turn-level detector plus rewrite pass improves measured faithfulness across five models and in- and out-of-domain documents.

  3. Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    AGAR uses middle-to-late layer attention in VLMs to identify and enlarge important word spans in rendered text images, improving performance on visual text comprehension benchmarks.

  4. Why Retrieval-Augmented Generation Fails: A Graph Perspective

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    Attribution graphs reveal that RAG failures arise from shallow fragmented evidence flow in LLMs, enabling topology-based detection and targeted interventions that reinforce question-guided routing.

  5. HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling

    cs.CV 2025-09 conditional novelty 6.0 of 10

    HiDe uses token-wise attention decoupling and layout-preserving decoupling to build compact crops that push Qwen2.5-VL and InternVL3 to state-of-the-art scores on high-resolution VQA benchmarks.

Pith tools