17 citations · 23 across the 4 of their papers we have counts for
7 papers · 1 filter
PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning
Tianci Xue, Ziqi Wang, Yixia Li +2
Instruction tuning enhances the instruction following ability of large language models by finetuning with supervised instruction data. Previous work proposes in-context instruction…
Enabling Language Models to Implicitly Learn Self-Improvement
Ziqi Wang, Le Hou, Tianjian Lu +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that ther…
Parameter-Efficient Tuning Helps Language Model Alignment
Tianci Xue, Ziqi Wang, Heng Ji
Aligning large language models (LLMs) with human preferences is essential for safe and useful LLMs. Previous works mainly adopt reinforcement learning (RLHF) and direct preference…
CLEVE: Contrastive Pre-training for Event Extraction
Ziqi Wang, Xiaozhi Wang, Xu Han +6
Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event cha…
MAVEN: A Massive General Domain Event Detection Dataset
Xiaozhi Wang, Ziqi Wang, Xu Han +7
Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text…
Learning from Explanations with Neural Execution Tree
Ziqi Wang, Yujia Qin, Wenxuan Zhou +5
While deep neural networks have achieved impressive performance on a range of NLP tasks, these data-hungry models heavily rely on labeled data, which restricts their applications i…