7 citations · 17 across the 12 of their papers we have counts for
12 papers
TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents
Yizhu Jiao, Sha Li, Sizhe Zhou +2
The task of information extraction (IE) is to extract structured knowledge from text. However, it is often not straightforward to utilize IE output due to the mismatch between the…
Establishing Knowledge Preference in Language Models
Sizhe Zhou, Sha Li, Yu Meng +3
Language models are known to encode a great amount of factual knowledge through pretraining. However, such knowledge might be insufficient to cater to user requests, requiring the…
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao, Ming Zhong, Sha Li +4
Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural…
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
Sha Li, Ruining Zhao, Manling Li +3
Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a lar…
SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design
Carl Edwards, Aakanksha Naik, Tushar Khot +3
Predicting synergistic drug combinations can help accelerate discovery of cancer treatments, particularly therapies personalized to a patient's specific tumor via biopsied cells. I…
GLEN: General-Purpose Event Detection for Thousands of Types
Qiusi Zhan, Sha Li, Kathryn Conger +3
The progress of event extraction research has been hindered by the absence of wide-coverage, large-scale datasets. To make event extraction systems more accessible, we build a gene…
Open Relation and Event Type Discovery with Type Abstraction
Sha Li, Heng Ji, Jiawei Han
Conventional closed-world information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied t…
Open-Vocabulary Argument Role Prediction for Event Extraction
Yizhu Jiao, Sha Li, Yiqing Xie +3
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies…
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
Jianhao Shen, Chenguang Wang, Ye Yuan +5
This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM…
Towards a Unified Multi-Dimensional Evaluator for Text Generation
Ming Zhong, Yang Liu, Da Yin +6
Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimen…
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization
Sha Li, Liyuan Liu, Yiqing Xie +2
We propose P4E, an identify-and-localize event detection framework that integrates the best of few-shot prompting and structured prediction. Our framework decomposes event detectio…