Abstract:

In this talk, / will present CARAG, a Context-Aware Retrieval Augmented Generation framework that improves Automated Fact Verification (AFV) by incorporating both local and global explanations. Unlike traditional factchecking methods that focus on isolated claims, CARAG leverages thematic embedding aggregation to verify claims in a broader contextual landscape. I will also introduce CARAG-u, an unsupervised extension that eliminates the need for predefined thematic annotations, dynamically deriving contextually relevant evidence clusters from unstructured data. CARAG-u maintains strong performance while increasing adaptability and scalability. Through benchmarks on the FactVer dataset, / will demonstrate how these frameworks enhance explainability and thematic coherence, advancing the role of Al in trustworthy, transparent fact verification.

Bio:

Dr. Héctor Allende-Cid is a Senior Researcher in the Natural Language Understanding Group at Fraunhofer IAIS since August 2023. He holds an Computer Science Engineering degree (2007), a Master (2009) and Doctorante in Computer Science (2015) from Universidad Técnica Federico Santa María, Chile, and has been a Full Professor at Pontificia Universidad Católica de Valparaíso since 2015. He served as President of the Chilean Pattern Recognition Association from 2017 until 2021. His research interests include NLP, Machine Learning, Time Series Forecasting, and Computer Vision.