4 citations · 9 across the 6 of their papers we have counts for
6 papers
Causal-Guided Active Learning for Debiasing Large Language Models
Li Du, Zhouhao Sun, Xiao Ding +5
Although achieving promising performance, recent analyses show that current generative large language models (LLMs) may still capture dataset biases and utilize them for generation…
Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning
Yang Zhao, Li Du, Xiao Ding +5
Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corp…
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis
Li Du, Yequan Wang, Xingrun Xing +4
Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs. T…
FLM-101B: An Open LLM and How to Train It with $100K Budget
Xiang Li, Yiqun Yao, Xin Jiang +10
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimod…
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
Li Du, Xiao Ding, Kai Xiong +2
Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can…
Modeling Event Background for If-Then Commonsense Reasoning Using Context-aware Variational Autoencoder
Li Du, Xiao Ding, Ting Liu +1
Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intent…