M^3Check: A Demonstration System for Multilingual Multi-clue Multi-Hop Fact-Checking

Imagine that, while we cannot simply Google a complex claim, we can still find some relevant clues to help us. The question is, how can we use these clues to verify the input claim? M^3Check is a RAG model that follows three major steps: 1) break down the claim into clues, 2) verify each clue based on articles, and 3) conclude, check, and reason the claim based on a structured, supervised, and verified chain of clues. This project leverages findings from multiple projects, offering a good example of how research outcomes can be applied to address a real-world problem.

Barid Xi Ai
Barid Xi Ai
Research Fellow

Postdoctoral Research Fellow at WING

Xinyuan Lu
ISEP Doctoral Alumnus (Aug ‘25). Thesis: Fact-Checking Complex Claims with Large Language Models.

PhD Candidate January 2020 Intake

Liangming Pan
Doctoral Alumnus (Apr ‘22). Thesis: Towards Generating Deep Questions from Text.

Doctoral Alumnus (Apr ‘22).

Sahej Agarwal
UROP Student (Jan ‘25) Thesis: LitePruner: A Lightweight Realtime Token Pruner before Large Language Models
VIP 10K Awardee

UROP Student

Mahardika Krisna Ihsani
Mahardika Krisna Ihsani
SRI Intern (Jul ‘24)

Summer Research Intern (SRI Programme)

Min-Yen Kan
Min-Yen Kan
Associate Professor

WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.