Evidence Piece
Evidence piece research focuses on improving the use of evidence in various AI applications, primarily aiming to enhance accuracy, trustworthiness, and explainability. Current research emphasizes developing methods for efficient evidence retrieval and selection, often employing techniques like contrastive learning, language model fine-tuning, and evidence theory, to improve fact-checking, clinical decision support, and other tasks. This work is significant because it addresses critical challenges in AI reliability and transparency, paving the way for more robust and trustworthy AI systems across diverse fields.
Papers
SoK: Cross-border Criminal Investigations and Digital Evidence
Fran Casino, Claudia Pina, Pablo López-Aguilar, Edgar Batista, Agusti Solanas, Constantinos Patsakis
End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
Barry Menglong Yao, Aditya Shah, Lichao Sun, Jin-Hee Cho, Lifu Huang