118 citations · 292 across the 13 of their papers we have counts for
13 papers · 1 filter
At Which Training Stage Does Code Data Help LLMs Reasoning?
Yingwei Ma, Yue Liu, Yue Yu +4
Large Language Models (LLMs) have exhibited remarkable reasoning capabilities and become the foundation of language technologies. Inspired by the great success of code data in trai…
ToolQA: A Dataset for LLM Question Answering with External Tools
Yuchen Zhuang, Yue Yu, Kuan Wang +2
Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning…
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling
Yuchen Zhuang, Yue Yu, Lingkai Kong +2
Learning from noisy labels is a challenge that arises in many real-world applications where training data can contain incorrect or corrupted labels. When fine-tuning language model…
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang +5
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored diff…
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
Yue Yu, Yuchen Zhuang, Rongzhi Zhang +3
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data…
COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning
Yue Yu, Chenyan Xiong, Si Sun +2
We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the generalization ability of dense retrieval by combating the distribution shifts between source tr…