198 citations · 562 across the 31 of their papers we have counts for
5 papers · 1 filter
CodeTaxo: Enhancing Taxonomy Expansion with Limited Examples via Code Language Prompts
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +3
Taxonomies play a crucial role in various applications by providing a structural representation of knowledge. The task of taxonomy expansion involves integrating emerging concepts…
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Herun Wan, Shangbin Feng, Zhaoxuan Tan +3
Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accu…
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +4
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making…
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Shangbin Feng, Herun Wan, Ningnan Wang +3
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we b…
Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
Shangbin Feng, Weijia Shi, Yike Wang +3
Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature…