Cross-lingual COVID-19 Fake News Detection

1 code implementation • 13 Oct 2021 • Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu

The COVID-19 pandemic poses a great threat to global public health.

Fact Checking Fake News Detection +1

Learning to Select from Multiple Options

1 code implementation • 1 Dec 2022 • Jiangshu Du, Wenpeng Yin, Congying Xia, Philip S. Yu

To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.

Entity Typing Intent Detection +2

All Labels Together: Low-shot Intent Detection with an Efficient Label Semantic Encoding Paradigm

no code implementations • 7 Sep 2023 • Jiangshu Du, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu

In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.

Domain Generalization Intent Detection

kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning

no code implementations • 17 Dec 2023 • Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip S. Yu

Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.

In-Context Learning Prompt Engineering +1

FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability

1 code implementation • 28 Feb 2024 • Congying Xia, Chen Xing, Jiangshu Du, Xinyi Yang, Yihao Feng, ran Xu, Wenpeng Yin, Caiming Xiong

This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents.