9 citations · 10 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
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…
Can LLMs Extract Frame-Semantic Arguments?
Jacob Devasier, Rishabh Mediratta, Chengkai Li
Frame-semantic parsing is a critical task in natural language understanding, yet the ability of large language models (LLMs) to extract frame-semantic arguments remains underexplor…
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…
Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
Kevin Meng, Damian Jimenez, Fatma Arslan +3
We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims. In this work, we introdu…