22 citations · 66 across the 8 of their papers we have counts for
8 papers
Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings
Tengyu Pan, Zhichao Duan, Zhenyu Li +4
Text embedding models are essential for various natural language processing tasks, enabling the effective encoding of semantic information into dense vector representations. These…
Learning Interpretable Rules for Scalable Data Representation and Classification
Zhuo Wang, Wei Zhang, Ning Liu +1
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Zhenyu Li, Sunqi Fan, Yu Gu +5
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language question…
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
Zhuo Wang, Rongzhen Li, Bowen Dong +8
Recent investigations show that large language models (LLMs), specifically GPT-4, not only have remarkable capabilities in common Natural Language Processing (NLP) tasks but also e…
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering
Zhichao Duan, Xiuxing Li, Zhengyan Zhang +3
Question Answering (QA) is the task of automatically answering questions posed by humans in natural languages. There are different settings to answer a question, such as abstractiv…
Effective Few-Shot Named Entity Linking by Meta-Learning
Xiuxing Li, Zhenyu Li, Zhengyan Zhang +5
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g.,…
Scalable Rule-Based Representation Learning for Interpretable Classification
Zhuo Wang, Wei Zhang, Ning Liu +1
Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. Howe…
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
Zhuo Wang, Wei Zhang, Ning Liu +1
Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance,…