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Ye Wang

4 papers hereh-index 338 citations4 works total

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

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.SD2
same name
  • Ye Wang — 24 papers, h 8
  • Ye Wang — 20 papers, h 21
  • Ye Wang — 15 papers, h 4
  • Ye Wang — 13 papers, h 6
  • Ye Wang — 10 papers
  • Ye Wang — 10 papers, h 2

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 citedTowards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning

1 citations · 2 across the 3 of their papers we have counts for

collaborators

4 papers

cs.SD2026

Unlocking Large Audio-Language Models for Interactive Language Learning

Hongfu Liu, Zhouying Cui, Xiangming Gu +1

Achieving pronunciation proficiency in a second language (L2) remains a challenge, despite the development of Computer-Assisted Pronunciation Training (CAPT) systems. Traditional C…

cs.CL2024★ 1 cited

Advancing Adversarial Suffix Transfer Learning on Aligned Large Language Models

Hongfu Liu, Yuxi Xie, Ye Wang +1

Language Language Models (LLMs) face safety concerns due to potential misuse by malicious users. Recent red-teaming efforts have identified adversarial suffixes capable of jailbrea…

cs.CL2023★ 1 cited

Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learning

Hongfu Liu, Ye Wang

Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveraging a few demonstrations pertaining to a new downstream task as conditions. Howeve…

cs.SD2023

Advancing Test-Time Adaptation in Wild Acoustic Test Settings

Hongfu Liu, Hengguan Huang, Ye Wang

Acoustic foundation models, fine-tuned for Automatic Speech Recognition (ASR), suffer from performance degradation in wild acoustic test settings when deployed in real-world scenar…

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