Fully Automated Fact Checking Using External Sources
Given the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, we propose a general-purpose framework for fully-automatic fact checking using external sources, tapping the potential of the entire Web as a knowledge source to confirm or reject a claim. Our framework uses a deep neural network with LSTM text encoding to combine semantic kernels with task-specific embeddings that encode a claim together with pieces of potentially-relevant text fragments from the Web, taking the source reliability into account. The evaluation results show good performance on two different tasks and datasets: (i) rumor detection and (ii) fact checking of the answers to a question in community question answering forums.
Code (1)
Tasks
Community Question AnsweringFact CheckingQuestion AnsweringSimilar Papers 제목 키워드 기반
Evaluating open-source Large Language Models for automated fact-checking
The increasing prevalence of online misinformation has heightened the demand for automated fact-checking solutions. Large Language Models (LLMs) have emerged as potential tools for assisting in this task, but their effec…
Fact CheckingMisinformationSemi-automated Fact-checking in Portuguese: Corpora Enrichment using Retrieval with Claim extraction
The accelerated dissemination of disinformation often outpaces the capacity for manual fact-checking, highlighting the urgent need for Semi-Automated Fact-Checking (SAFC) systems. Within the Portuguese language context, …
Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs
Large Language Models (LLMs) augmented with retrieval mechanisms have demonstrated significant potential in fact-checking tasks by integrating external knowledge. However, their reliability decreases when confronted with…
Fact CheckingRAGRetrievalRetrieval-augmented GenerationFactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking
Automatic fact-checking plays a crucial role in combating the spread of misinformation. Large Language Models (LLMs) and Instruction-Following variants, such as InstructGPT and Alpaca, have shown remarkable performance i…
Fact CheckingInstruction FollowingLanguage ModelingLanguage Modelling+1REFLEX: Self-Refining Explainable Fact-Checking via Verdict-Anchored Style Control
The prevalence of fake news on social media demands automated fact-checking systems to provide accurate verdicts with faithful explanations. However, existing large language model (LLM)-based approaches ignore deceptive …