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Ye Yuan

6 papers hereh-index 592 citations14 works total

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

author position
  • middle author5

Across the 5 of 6 papers where every author was matched, so the position is known.

fields
  • cs.DB3
  • cs.AI1
  • cs.CL1
  • cs.LG1
same name
  • Ye Yuan — 22 papers, h 22
  • Ye Yuan — 20 papers, h 26
  • Ye Yuan — 19 papers, h 12
  • Ye Yuan — 16 papers, h 11
  • Ye Yuan — 16 papers, h 16
  • Ye Yuan — 13 papers, h 6

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
20242026
most citedHarnessing Diversity for Important Data Selection in Pretraining Large Language Models

3 citations · 4 across the 3 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.DB2025

Unstructured Data Analysis using LLMs: A Comprehensive Benchmark

Qiyan Deng, Jianhui Li, Chengliang Chai +9

Nowadays, the explosion of unstructured data presents immense analytical value. Leveraging the remarkable capability of large language models (LLMs) in extracting attributes of str…

cs.DB2025★ 1 cited

QUEST: Query Optimization in Unstructured Document Analysis

Zhaoze Sun, Qiyan Deng, Chengliang Chai +6

Most recently, researchers have started building large language models (LLMs) powered data systems that allow users to analyze unstructured text documents like working with a datab…

cs.CL2025

Not All Documents Are What You Need for Extracting Instruction Tuning Data

Chi Zhang, Huaping Zhong, Hongtao Li +11

Instruction tuning improves the performance of large language models (LLMs), but it heavily relies on high-quality training data. Recently, LLMs have been used to synthesize instru…

cs.LG2025

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization

Kuan Zhang, Chengliang Chai, Jingzhe Xu +5

Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noi…

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