<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Fact-Checking | Web IR NLP Group @ NUS</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/tag/fact-checking/</link><atom:link href="https://www.comp.nus.edu.sg/~kanmy/wing/tag/fact-checking/index.xml" rel="self" type="application/rss+xml"/><description>Fact-Checking</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 01 Jan 2025 00:00:00 +0000</lastBuildDate><image><url>https://www.comp.nus.edu.sg/~kanmy/wing/media/icon_hub1b49484bccf4f586453e7d2a784a896_225489_512x512_fill_lanczos_center_3.png</url><title>Fact-Checking</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/tag/fact-checking/</link></image><item><title>M^3Check: A Demonstration System for Multilingual Multi-clue Multi-Hop Fact-Checking</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/project/m3check/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/project/m3check/</guid><description>&lt;p>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.&lt;/p></description></item><item><title>TART – An Open-Source Tool-Augmented Framework for Explainable Table-Based Reasoning</title><link>https://www.comp.nus.edu.sg/~kanmy/wing/project/tart/</link><pubDate>Wed, 01 Jan 2025 00:00:00 +0000</pubDate><guid>https://www.comp.nus.edu.sg/~kanmy/wing/project/tart/</guid><description>&lt;p>TART is designed to overcome the &lt;strong>limitations of LLMs in table reasoning&lt;/strong> by combining &lt;strong>structured table processing, precise tool execution, and explainability&lt;/strong>. The key components of TART are:&lt;/p>
&lt;ol>
&lt;li>&lt;strong>Table Formatter&lt;/strong>: Prepares tables by formatting, aligning, and cleaning the data for structured input.&lt;/li>
&lt;li>&lt;strong>Tool Maker&lt;/strong>: Dynamically generates tools (Python functions) tailored for complex table operations.&lt;/li>
&lt;li>&lt;strong>Explanation Generator&lt;/strong>: Integrates structured reasoning steps and tool outputs into human-readable explanations.&lt;/li>
&lt;/ol>
&lt;p>TART is evaluated on &lt;strong>nine table-based reasoning benchmarks&lt;/strong>, including &lt;strong>SCITAB, TabFact, FinQA, and HybridQA&lt;/strong>. The framework consistently outperforms Chain-of-Thought (CoT) prompting by integrating structured tool-assisted reasoning.&lt;/p>
&lt;p>TART has received the &lt;strong>Best Paper Runner-Up Award&lt;/strong> at the &lt;a href="https://table-representation-learning.github.io/NeurIPS2024/" target="_blank" rel="noopener">3rd Table Representation Learning Workshop&lt;/a> at NeurIPS 2024 and has been accepted at &lt;strong>NAACL 2025&lt;/strong>. Its code and dataset are publicly available for further research. Researchers working on LLM explainability, table reasoning, and AI-assisted fact-checking are encouraged to contribute.&lt;/p></description></item></channel></rss>