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Hang Su

19 papers hereh-index 12512 citations28 works total

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

author position
  • first author1
  • middle author15
  • last author3

Across the 19 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.AI4
  • cs.CL4
  • cs.CR1
  • cs.LG1
  • cs.MM1
same name
  • Hang Su — 16 papers, h 11
  • Hang Su — 16 papers, h 8
  • Hang Su — 10 papers, h 9
  • Hang Su — 8 papers, h 1
  • Hang Su — 8 papers, h 5
  • Hang Su — 7 papers, h 5

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
20242026
collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models

Siyuan Zhang, Jialian Li, Yichi Zhang +3

Large Language Models have achieved remarkable performance on reasoning tasks, motivating research into how this ability evolves during training. Prior work has primarily analyzed…

cs.CL2025

Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization

Siyuan Zhang, Yichi Zhang, Yinpeng Dong +1

Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of factual hallucinations, which can be difficult to detect and mi…

cs.CL2025

Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation

Yichi Zhang, Yao Huang, Yifan Wang +10

The trustworthiness of Multimodal Large Language Models (MLLMs) remains an intense concern despite the significant progress in their capabilities. Existing evaluation and mitigatio…

cs.CL2024

MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models

Yichi Zhang, Yao Huang, Yitong Sun +10

Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature…

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