The paper introduces a new benchmark for multi-target cross-lingual summarization (MTXLS) and proposes an activation steering method that significantly improves LLM performance by guiding the generati…

The paper introduces ChunkGroupSHAP, a listwise Shapley method that clusters semantantly related chunks into shared cross-document features for dense semantic ranking.

The paper proposes a novel KAN-enhanced BiGRU architecture to improve legal document classification and summarization in a low-resource, multilingual setting using Bengali and English legal texts.

The paper presents CAPE, a framework that generates natural-language explanations for spatially organized document layouts, using context-aware representations and LLM-based explanation generation.

This paper proposes a framework for summarizing dialogues, modeling semantic and emotion dynamics using multimodal inputs and an adapted hierarchical Chain-of-Agents approach.

This paper introduces SVD-RAG, a cost-efficient and content-adaptive summarization method for hierarchical Retrieval-Augmented Generation systems using Singular Value Decomposition on dense sentence e…

This paper evaluates the effectiveness of cluster-based semantic chunking compared to fixed-size and recursive chunking in Retrieval-Augmented Generation systems using the Retrieval Augmented Generati…

Xetrieval introduces an embedding-level framework to mechanistically explain dense retrieval decisions by decomposing high-dimensional embeddings into sparse, human-interpretable features.

This paper evaluates the use of AI-generated summaries on search engine results pages (SERPs) in academic search for social science information.

The paper introduces a supervised fine-tuning pipeline using large language models to accurately categorize sentence-level clinical provenance across multi-disciplinary hospital notes, demonstrating t…

The paper compares two families of methods for ranking case-law sentences by their usefulness for explaining statutory concepts using ModernBERT and decoder-only models. Decoder-only models achieve th…

The paper introduces I-WebGenBench, a framework and benchmark that converts static scientific papers into executable, interactive web systems, allowing users to dynamically explore the paper's mechani…

The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.

The paper introduces TELL, a novel explainable AI-generated text detection architecture that provides detailed, human-understandable explanations for its scores, achieving competitive performance whil…

This paper introduces a new benchmark dataset and evaluation framework for 'data snapshot extraction,' focusing on identifying and localizing semantically meaningful analytical artifacts within operat…

The authors introduce Structured PubMed, a comprehensive corpus of section-labeled biomedical abstracts compiled from the complete PubMed database.

The paper enhances French parsing accuracy by integrating data from a syntactic lexicon and applying word clustering methods to verbs within a Probabilistic Context-Free Grammar framework.

TalTech submitted top-ranking systems to the Beyond Transcription Challenge using fine-tuned Voxtral models and reinforcement learning against Open Medical Concept F1.

This paper presents the benefits of visual pretraining for foundation model intelligence, outperforming text-only pretraining on multiple backbones and benchmarks.