activity
20222024
most citedLLMaAA: Making Large Language Models as Active Annotators

3 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph

Xuan Yi, Yanzeng Li, Lei Zou

Multi-modal knowledge graphs have emerged as a powerful approach for information representation, combining data from different modalities such as text, images, and videos. While se…

cs.CL20233 cited

LLMaAA: Making Large Language Models as Active Annotators

Ruoyu Zhang, Yanzeng Li, Yongliang Ma +2

Prevalent supervised learning methods in natural language processing (NLP) are notoriously data-hungry, which demand large amounts of high-quality annotated data. In practice, acqu…

cs.CL2023

Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

Minhao Zhang, Yongliang Ma, Yanzeng Li +3

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions. To utilize multiple knowledge bases (KB), previous works merge all KBs…

cs.CL2023

ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources

Yirui Zhan, Yanzeng Li, Minhao Zhang +1

With the introduction of deep learning models, semantic parsingbased knowledge base question answering (KBQA) systems have achieved high performance in handling complex questions.…

cs.CL2022

Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base

Minhao Zhang, Ruoyu Zhang, Yanzeng Li +1

Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect an…