Fact-checking is an essential task in NLP that is commonly utilized for validating the factual
accuracy of claims. Prior work has mainly focused on fine-tuning pre-trained languages …

Large language models (LLMs), especially when instruction-tuned for chat, have become
part of our daily lives, freeing people from the process of searching, extracting, and …

Existing metrics for evaluating the factuality of long-form text, such as FACTSCORE (Min et
al., 2023) and SAFE (Wei et al., 2024), decompose an input text into" atomic claims" and …

Abstract Large Language Models (LLMs) are prone to factuality hallucination, generating
text that contradicts established knowledge. While extensive research has addressed this in …

Automated Fact-Checking (AFC) is the automated verification of claim accuracy. AFC is
crucial in discerning truth from misinformation, especially given the huge amounts of content …

Large language models (LLMs), especially when instruction-tuned for chat, have become
part of our daily lives, freeing people from the process of searching, extracting, and …

Modern language models (LMs) pose a new challenge in capability assessment. Static
benchmarks inevitably saturate without providing confidence in the deployment tolerances …

Automatic factuality verification of large language model (LLM) generations is becoming
more and more widely used to combat hallucinations. A major point of tension in the …

Uncertainty quantification (UQ) is a critical component of machine learning (ML)
applications. The rapid proliferation of large language models (LLMs) has stimulated …

We have witnessed lately a rapid proliferation of advanced Large Language Models (LLMs)
capable of generating high-quality text. While these LLMs have revolutionized text …