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Kun Zhu

9 papers hereh-index 7119 citations11 works total

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

author position
  • first author3
  • middle author6

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL7
  • cs.LG1
  • cs.SE1
same name
  • Kun Zhu — 6 papers, h 7
  • Kun Zhu — 5 papers, h 3
  • Kun Zhu — 4 papers, h 1
  • Kun Zhu — 3 papers, h 3
  • Kun Zhu — 3 papers, h 2
  • Kun Zhu — 2 papers, h 15

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 citedLearning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System

2 citations · 2 across the 8 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024

Length Controlled Generation for Black-box LLMs

Yuxuan Gu, Wenjie Wang, Xiaocheng Feng +5

Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is…

cs.LG2024

Discrete Modeling via Boundary Conditional Diffusion Processes

Yuxuan Gu, Xiaocheng Feng, Lei Huang +5

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the…

cs.CL2024

An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation

Kun Zhu, Xiaocheng Feng, Xiyuan Du +7

Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when con…

cs.CL2024

BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering

Zheng Chu, Jingchang Chen, Qianglong Chen +6

Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval…

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