1.2k citations · 1.3k across the 35 of their papers we have counts for
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Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
Yang Zhao, Li Du, Xiao Ding +3
LLMs' performance on complex tasks is still unsatisfactory. A key issue is that presently LLMs learn in a data-driven schema, while the instructions about these complex tasks are b…
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…
Diagnosing and Remedying Knowledge Deficiencies in LLMs via Label-free Curricular Meaningful Learning
Kai Xiong, Xiao Ding, Li Du +4
Large Language Models (LLMs) are versatile and demonstrate impressive generalization ability by mining and learning information from extensive unlabeled text. However, they still e…
Enhancing Complex Causality Extraction via Improved Subtask Interaction and Knowledge Fusion
Jinglong Gao, Chen Lu, Xiao Ding +3
Event Causality Extraction (ECE) aims at extracting causal event pairs from texts. Despite ChatGPT's recent success, fine-tuning small models remains the best approach for the ECE…
Self-Evolving GPT: A Lifelong Autonomous Experiential Learner
Jinglong Gao, Xiao Ding, Yiming Cui +4
To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts. However, they rely on manual…
Beyond Fixed Length: Bucket Pre-training is All You Need
Qing Yang, Qiyao Peng, Hongtao Liu +3
Large Language Models (LLMs) have demonstrated exceptional performance across various tasks, with pre-training stage serving as the cornerstone of their capabilities. However, the…
Towards Generalizable and Faithful Logic Reasoning over Natural Language via Resolution Refutation
Zhouhao Sun, Xiao Ding, Li Du +4
Large language models (LLMs) have achieved significant performance in various natural language reasoning tasks. However, they still struggle with performing first-order logic reaso…
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
Yirong Zeng, Xiao Ding, Yi Zhao +5
Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking syst…
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
Kai Xiong, Xiao Ding, Ting Liu +5
Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking huma…
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…