most citedTwo-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

3 citations · 5 across the 5 of their papers we have counts for

collaborators
Showing cs.CLShow all

5 papers · 1 filter

cs.CL2024★ 1 cited

Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing

Wenyuan Zhang, Shuaiyi Nie, Jiawei Sheng +4

Large language model (LLM) role-playing has gained widespread attention. Authentic character knowledge is crucial for constructing realistic LLM role-playing agents. However, exist…

cs.CL2024

Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning

Yongquan He, Wenyuan Zhang, Xuancheng Huang +5

Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual in…

cs.CL2024★ 3 cited

Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models

Yifu Gao, Linbo Qiao, Zhigang Kan +3

Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic stru…

cs.CL2023★ 1 cited

A Boundary Offset Prediction Network for Named Entity Recognition

Minghao Tang, Yongquan He, Yongxiu Xu +3

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER t…

cs.CL2023

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning

Minghao Tang, Yongquan He, Yongxiu Xu +3

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge kn…