Accepted Papers

Using large language models (LLMs) to annotate relevance is an increasingly important problem in the information retrieval community. While some studies demonstrate that LLMs can achieve high user agreement with ground truth (human) judgments, other studies have argued for the opposite conclusion. T...

With the increasing use of AI, deepfakes are becoming an increasingly prevalent threat in today's world. At the same time, the performance of most detectors drops significantly when faced with unseen data, whereas generation models are improving, resulting in fewer artefacts. We examined deepfakes p...

This paper introduces XProvence, a multilingual zero-cost context pruning model for Retrieval-Augmented Generation (RAG), supporting 100+ languages. Motivated by the growing use of RAG systems across diverse languages, we explore several strategies to generalize the Provence framework--which first i...

Formula retrieval is an important topic in Mathematical Information Retrieval. We propose SSEmb, a novel embedding framework capable of capturing both structural and semantic features of formulas. Structurally, we employ Graph Contrastive Learning to encode formulas represented as Shared-substructur...

Modern representation learning increasingly relies on unsupervised and self-supervised methods trained on large-scale unlabeled data. While these approaches achieve impressive generalization across tasks and domains, evaluating embedding quality without labels remains an open challenge. In this work...