13 citations · 43 across the 29 of their papers we have counts for
8 papers · 1 filter
QUILL: Quotation Generation Enhancement of Large Language Models
Jin Xiao, Bowei Zhang, Qianyu He +6
While Large language models (LLMs) have become excellent writing assistants, they still struggle with quotation generation. This is because they either hallucinate when providing f…
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
Chengyu Du, Jinyi Han, Yizhou Ying +9
Recent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful ou…
Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
Yikai Zhang, Qianyu He, Xintao Wang +3
Multi-Modal Knowledge Graphs (MMKGs) have proven valuable for various downstream tasks. However, scaling them up is challenging because building large-scale MMKGs often introduces…
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.…
From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
Qianyu He, Jie Zeng, Qianxi He +2
It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following). Yet, it remains under-explored how to en…
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Wenhao Huang, Qianyu He, Zhixu Li +2
Definition bias is a negative phenomenon that can mislead models. Definition bias in information extraction appears not only across datasets from different domains but also within…
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
Yuncheng Huang, Qianyu He, Yipei Xu +2
Current language models have demonstrated their capability to develop basic reasoning, but struggle in more complicated reasoning tasks that require a combination of atomic skills,…
Small Language Model Can Self-correct
Haixia Han, Jiaqing Liang, Jie Shi +2
Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is gener…