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

Wed, 01 Jan 2025 00:00:00 +0000

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.

Model-generated Code-mixed Sentences

Wed, 01 Jan 2025 00:00:00 +0000

Code-switching/code-mixing is a common linguistic practice where speakers switch between multiple languages in a single discourse. Developing language models with code-switching capabilities is crucial to the interest of the large multilingual communities. However, collecting real-world code-mixed sentences for training remains challenging due to its colloquial nature, highlighting the importance of synthetic code-mixed data. Preliminary experiments shows LLMs can generate code-mixed sentences by switching entities in multiple languages. However, these model-generated sentences are often not natural limiting further leverage for training. This project explores methods to improve the naturalness of model-generated code-mixed sentences. Ultimately, we aim to build an automated pipeline capable of generating natural-sounding code-switched sentences for further downstream tasks.

Multilingual NLP | Web IR NLP Group @ NUS

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

Model-generated Code-mixed Sentences