Fact Checking
Fact-checking research aims to automate the verification of claims, combating the spread of misinformation across various media. Current efforts focus on improving evidence retrieval using techniques like contrastive learning and leveraging large language models (LLMs) for claim verification and explanation generation, often incorporating knowledge graphs and multimodal data (text and images). These advancements are crucial for enhancing the accuracy and efficiency of fact-checking, with implications for journalism, public health communication, and broader efforts to mitigate the impact of misinformation.
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Enhancing Natural Language Inference Performance with Knowledge Graph for COVID-19 Automated Fact-Checking in Indonesian Language
Arief Purnama Muharram, Ayu PurwariantiEvidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
Ronit Singhal, Pransh Patwa, Parth Patwa, Aman Chadha, Amitava Das