11 citations · 23 across the 15 of their papers we have counts for
5 papers · 2 filters
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
Wanqi Yang, Yanda Li, Meng Fang +1
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive quest…
Continual Learning for Temporal-Sensitive Question Answering
Wanqi Yang, Yunqiu Xu, Yanda Li +3
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). Previous research has primarily focused on Temporal Se…
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Zepeng Ding, Ruiyang Ke, Wenhao Huang +4
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex…
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Dixuan Wang, Yanda Li, Junyuan Jiang +5
Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate re…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Yanda Li, Dixuan Wang, Jiaqing Liang +4
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.…