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researcher

Jun 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 author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.IR1
same name
  • Jun Wang — 64 papers, h 35
  • Jun Wang — 55 papers, h 47
  • Jun Wang — 27 papers, h 12
  • Jun Wang — 20 papers, h 4
  • Jun Wang — 18 papers, h 11
  • Jun Wang — 17 papers, h 11

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

most citedSQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy

2 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

cs.IR2025

Coherency Improved Explainable Recommendation via Large Language Model

Shijie Liu, Ruixing Ding, Weihai Lu +4

Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rati…

cs.CL2024

CNNSum: Exploring Long-Context Summarization with Large Language Models in Chinese Novels

Lingxiao Wei, He Yan, Xiangju Lu +3

Large language models (LLMs) have been well-researched in various long-context tasks. However, the scarcity of long-context summarization datasets hinders progress in this area. To…

cs.CL2024

E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning

Zihan Liao, Jun Wang, Hang Yu +3

Processing long contexts is increasingly important for Large Language Models (LLMs) in tasks like multi-turn dialogues, code generation, and document summarization. This paper addr…

cs.CL2024★ 2 cited

SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy

Tingkai Zhang, Chaoyu Chen, Cong Liao +6

Text-to-SQL conversion is a critical innovation, simplifying the transition from complex SQL to intuitive natural language queries, especially significant given SQL's prevalence in…

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