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Shu Yang

61 papers hereh-index 171k citations69 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author12
  • middle author45

Across the 59 of 61 papers where every author was matched, so the position is known.

fields
  • cs.CL38
  • cs.AI7
  • cs.LG7
  • cs.CV4
  • cs.CR2
  • cs.MA1
same name
  • Shu Yang — 74 papers, h 28
  • Shu Yang — 19 papers, h 8
  • Shu Yang — 12 papers, h 7
  • Shu Yang — 12 papers, h 4
  • Shu Yang — 12 papers, h 8
  • Shu Yang — 6 papers, h 77

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedIs ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation

58 citations · 112 across the 58 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Forged Peer Judgments Mislead Multimodal LLM Judge Panels: Source-Blind Anchoring and Panel-Consensus Verification

Yang Shu

Multimodal LLM judge panels can cross-reference peers, but a quoted peer judgment may itself be untrusted. We expose source-blind anchoring as a text-level attack surface in vision…

cs.CV2026

Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images

Qishun Yang, Shu Yang, Lijie Hu +1

Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods require explicit safety labels or c…

cs.CV2025

Benchmarking and Mitigating Sycophancy in Medical Vision Language Models

Juangui Xu, Zikun Guo, Jingwei Lv +5

Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite this serious threat to patient safety, a…

cs.CV2025

Stable Vision Concept Transformers for Medical Diagnosis

Lijie Hu, Songning Lai, Yuan Hua +3

Transparency is a paramount concern in the medical field, prompting researchers to delve into the realm of explainable AI (XAI). Among these XAI methods, Concept Bottleneck Models…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.