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researcher

Yuhao Wang

4 papers here

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

author position
  • first author1
  • middle author3

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

fields
  • cs.IR2
  • cs.CL1
  • cs.SD1
ORCID 0000-0002-6051-8659
same name
  • Yuhao Wang — 9 papers
  • Yuhao Wang — 8 papers
  • Yuhao Wang — 7 papers, h 12
  • Yuhao Wang — 6 papers, h 15
  • Yuhao Wang — 4 papers
  • Yuhao Wang — 3 papers, h 14

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
20232025
most citedExploration and Regularization of the Latent Action Space in Recommendation

41 citations · 43 across the 4 of their papers we have counts for

collaborators

4 papers

cs.SD2025★ 1 cited

SOVA-Bench: Benchmarking the Speech Conversation Ability for LLM-based Voice Assistant

Yixuan Hou, Heyang Liu, Yuhao Wang +5

Thanks to the steady progress of large language models (LLMs), speech encoding algorithms and vocoder structure, recent advancements have enabled generating speech response directl…

cs.CL2025★ 1 cited

VocalNet: Speech LLM with Multi-Token Prediction for Faster and High-Quality Generation

Yuhao Wang, Heyang Liu, Ziyang Cheng +4

Speech large language models (LLMs) have emerged as a prominent research focus in speech processing. We introduce VocalNet-1B and VocalNet-8B, a series of high-performance, low-lat…

cs.IR2024

Self-Calibrated Listwise Reranking with Large Language Models

Ruiyang Ren, Yuhao Wang, Kun Zhou +5

Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passa…

cs.IR2023★ 41 cited

Exploration and Regularization of the Latent Action Space in Recommendation

Shuchang Liu, Qingpeng Cai, Bowen Sun +7

In recommender systems, reinforcement learning solutions have effectively boosted recommendation performance because of their ability to capture long-term user-system interaction.…

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