5 papers · 1 filter
Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison
Pratuat Amatya, Vinay Setty, V Venktesh +1
Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment steps such as RLHF do not focus o…
Calibrating Small Language Models for Claim Check-Worthiness Detection
Pratuat Amatya, V Venktesh, Vinay Setty
Assessing claim check-worthiness is an essential first step in automated fact-checking pipelines. This work is motivated by a real deployment challenge at an early-stage startup: r…
Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs
Pratuat Amatya, Vinay Setty
We present a multilingual fact-checking system deployed at Factiverse, designed for high-throughput and low-latency operation across diverse languages. The system follows a modular…
The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking
Julia Maria Struß, Sebastian Schellhammer, Stefan Dietze +9
The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of langua…
One LLM to Train Them All: Multi-Task Learning Framework for Fact-Checking
Malin Astrid Larsson, Harald Fosen Grunnaleite, Vinay Setty
Large language models (LLMs) are reshaping automated fact-checking (AFC) by enabling unified, end-to-end verification pipelines rather than isolated components. While large proprie…