7 citations · 22 across the 18 of their papers we have counts for
11 papers · 1 filter
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Yichi Zhang, Zhuo Chen, Lingbing Guo +8
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research. However, the lack of factual accuracy is still a dark cloud h…
OneKE: A Dockerized Schema-Guided LLM Agent-based Knowledge Extraction System
Yujie Luo, Xiangyuan Ru, Kangwei Liu +10
We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news,…
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs
Jintian Zhang, Cheng Peng, Mengshu Sun +6
Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in…
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
Lei Liang, Mengshu Sun, Zhengke Gui +16
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, inc…
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Ningyu Zhang, Zekun Xi, Yujie Luo +11
Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs…
Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach
Zhouyu Jiang, Mengshu Sun, Lei Liang +1
Multi-hop question answering is a challenging task with distinct industrial relevance, and Retrieval-Augmented Generation (RAG) methods based on large language models (LLMs) have b…
Efficient Knowledge Infusion via KG-LLM Alignment
Zhouyu Jiang, Ling Zhong, Mengshu Sun +5
To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and ef…
Continual Few-shot Event Detection via Hierarchical Augmentation Networks
Chenlong Zhang, Pengfei Cao, Yubo Chen +4
Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce con…
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning
Zhitao He, Pengfei Cao, Zhuoran Jin +5
Event Causality Identification (ECI) refers to the detection of causal relations between events in texts. However, most existing studies focus on sentence-level ECI with high-resou…
ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models
Jun Xu, Mengshu Sun, Zhiqiang Zhang +1
Recent advancements in large language models have shown impressive performance in general chat. However, their domain-specific capabilities, particularly in information extraction,…
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Honghao Gui, Lin Yuan, Hongbin Ye +4
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that…