The large and ever-increasing amount of data available on the Internet, coupled with the laborious task of manual claim and fact verification, has sparked interest in the development of automated claim verification systems. Several deep learning and transformer-based models have been proposed for this task over the years. With the introduction of Large Language Models (LLMs) and their superior performance in several NLP tasks, we have seen a surge of LLM-based approaches to claim verification along with the use of novel methods such as Retrieval Augmented Generation (RAG). In this survey, we present a comprehensive account of recent claim verification frameworks using LLMs. We describe the different components of the claim verification pipeline used in these frameworks in detail, including common approaches to retrieval, prompting, and fine-tuning. Finally, we describe publicly available English datasets for this task.

Identifying relevant text spans is important for several downstream tasks in NLP, as it contributes to model explainability. While most span identification approaches rely on relatively smaller pre-trained language models like BERT, a few recent approaches have leveraged the latest generation of Large Language Models (LLMs) for the task. Current work has focused on explicit span identification like Named Entity Recognition (NER), while more subjective span identification with LLMs in tasks like Aspect-based Sentiment Analysis (ABSA) has been underexplored. In this paper, we fill this important gap by presenting an evaluation of the performance of various LLMs on text span identification in three popular tasks, namely sentiment analysis, offensive language identification, and claim verification. We explore several LLM strategies like instruction tuning, in-context learning, and chain of thought. Our results indicate underlying relationships within text aid LLMs in identifying precise text spans.

This paper describes the team GMU-MU submission to the Financial Misinformation Detection challenge. The goal of this challenge is to identify financial misinformation and generate explanations justifying the predictions by developing or adapting LLMs. The participants were provided with a dataset of financial claims that were categorized into six financial domain categories. We experiment with the Llama model using two approaches; instruction-tuning the model with the training dataset, and a prompting approach that directly evaluates the off-the-shelf model. Our best system was placed 5th among the 12 systems, achieving an overall evaluation score of 0.6682.

The advancements in time-efficient data collection techniques such as active learning (AL) has become salient for user intent classification performance in task-oriented dialog systems (TODS). In realistic settings, however, traditional AL techniques often fail to efficiently select targeted in-distribution (IND) data when encountering newly acquired out-of-distribution (OOD) user intents in the unlabeled pool. In this paper, we introduce a novel AL framework viz., AOSAL for TODS that combines a distance-based OOD detector using adaptive false positive rate threshold with an informativeness measure (e.g., entropy) to strategically select informative IND data points in the unlabeled pool. Specifically, we utilize the adaptive OOD detector to classify and filter out OOD samples from the unlabeled pool, then prioritize the acquisition of classified IND instances based on their informativeness scores. To validate our approach, we conduct experiments that display our framework’s flexibility and performance over multiple distance-based approaches and informativeness measures against deep AL baselines on benchmark text datasets. The results suggest that our AOSAL approach consistently outperforms the baselines on IND classification and OOD detection, advancing knowledge on improving robustness of task-oriented dialog systems.