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Yan Zhang

6 papers hereh-index 4203 citations13 works total

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

author position
  • middle author3
  • last author3

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.IR1
  • cs.LG1
same name
  • Yan Zhang — 88 papers, h 24
  • Yan Zhang — 83 papers
  • Yan Zhang — 59 papers, h 76
  • Yan Zhang — 54 papers
  • Yan Zhang — 46 papers
  • Yan Zhang — 28 papers, h 44

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 Verifiable Text Generation with Evolving Memory and Self-Reflection

3 citations · 3 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

Hao Sun, Yingyan Hou, Jiayan Guo +4

Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing t…

cs.CL2025

ZeroSearch: Incentivize the Search Capability of LLMs without Searching

Hao Sun, Zile Qiao, Jiayan Guo +7

Effective information searching is essential for enhancing the reasoning and generation capabilities of large language models (LLMs). Recent research has explored using reinforceme…

cs.CL2024

Retrieved In-Context Principles from Previous Mistakes

Hao Sun, Yong Jiang, Bo Wang +4

In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to…

cs.CL2023★ 3 cited

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection

Hao Sun, Hengyi Cai, Bo Wang +5

Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also know…

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