3 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
Not All Instances Are Equally Valuable: Towards Influence-Weighted Dataset Distillation
Qiyan Deng, Changqian Zheng, Lianpeng Qiao +3
Dataset distillation condenses large datasets into synthetic subsets, achieving performance comparable to training on the full dataset while substantially reducing storage and comp…
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…
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…
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…
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…