1 citations · 3 across the 10 of their papers we have counts for
5 papers · 1 filter
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji +3
Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face…
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
Wanlong Liu, Enqi Zhang, Li Zhou +5
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main…
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Wanlong Liu, Li Zhou, Dingyi Zeng +6
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To addr…
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Li Zhou, Taelin Karidi, Wanlong Liu +5
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our…
MLPs Compass: What is learned when MLPs are combined with PLMs?
Li Zhou, Wenyu Chen, Yong Cao +3
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain deriv…