7 papers · 1 filter
What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation
Qing Wang, Jacob Devasier, Chengkai Li
We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectories -- the order in which tokens…
CaseFacts: A Benchmark for Legal Fact-Checking and Precedent Retrieval
Akshith Reddy Putta, Jacob Devasier, Chengkai Li
Automated Fact-Checking has largely focused on verifying general knowledge against static corpora, overlooking high-stakes domains like law where truth is evolving and technically…
Reasoning or Rationalization? The Role of Justifications in Masked Diffusion Models for Fact Verification
Jacob Devasier
Unlike autoregressive models, which generate tokens sequentially and benefit from reasoning-before-answering strategies such as Chain-of-Thought, Masked Diffusion Language Models (…
Frame-Guided Synthetic Claim Generation for Automatic Fact-Checking Using High-Volume Tabular Data
Jacob Devasier, Akshith Putta, Qing Wang +2
Automated fact-checking benchmarks have largely ignored the challenge of verifying claims against real-world, high-volume structured data, instead focusing on small, curated tables…
ClaimCheck: Real-Time Fact-Checking with Small Language Models
Akshith Reddy Putta, Jacob Devasier, Chengkai Li
We introduce ClaimCheck, an LLM-guided automatic fact-checking system designed to verify real-world claims using live Web evidence and small language models. Unlike prior systems t…
Task-Oriented Automatic Fact-Checking with Frame-Semantics
Jacob Devasier, Rishabh Mediratta, Akshith Putta +1
We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them…