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Hang Yan

12 papers hereh-index 7247 citations14 works total

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

author position
  • middle author9
  • last author1

Across the 10 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL7
  • cs.AI3
  • cs.CV1
  • cs.LG1
same name
  • Hang Yan — 47 papers, h 24
  • Hang Yan — 14 papers, h 5
  • Hang Yan — 7 papers, h 7
  • Hang Yan — 6 papers, h 3
  • Hang Yan — 6 papers, h 3
  • Hang Yan — 5 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
most citedEasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

6 citations · 7 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024

Length Generalization of Causal Transformers without Position Encoding

Jie Wang, Tao Ji, Yuanbin Wu +5

Generalizing to longer sentences is important for recent Transformer-based language models. Besides algorithms manipulating explicit position features, the success of Transformers…

cs.CL2024★ 6 cited

EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

Weikang Zhou, Xiao Wang, Limao Xiong +18

Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibi…

cs.CV2024

MouSi: Poly-Visual-Expert Vision-Language Models

Xiaoran Fan, Tao Ji, Changhao Jiang +21

Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issu…

cs.CL2024

Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback

Songyang Gao, Qiming Ge, Wei Shen +9

The success of AI assistants based on Language Models (LLMs) hinges on Reinforcement Learning from Human Feedback (RLHF) to comprehend and align with user intentions. However, trad…

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