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…

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

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

The paper introduces FOSSIL, a new multilingual dataset and specialized workflow designed to significantly improve the extraction of citations embedded within complex footnotes common in law and human…

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 a robust four-stage NLP framework that uses schema-constrained LLMs and ESCO vocabulary to accurately extract and align educational competencies with labor market demands, quantif…

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…

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…

The paper introduces Ryze, an automated system that synthesizes evidence-enriched Question-Answering (QA) pairs from raw biomedical papers, resulting in a specialized VLM (BioVLM-8B) that significantl…

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 explores the use of legal nuggets for dense retrieval over Brazilian legal collections, achieving significant improvements in two datasets.

This paper presents an improved parallel version of MaxFEM algorithm for maximal frequent episode mining, achieving up to 8x speedup in C++ implementation and 35x improvement overall.

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 proposes a multi-turn retrieval-augmented generation pipeline for conversational systems across four domains.

This paper proposes an approach for fine-grained sentiment analysis using regression and extraction tasks, involving a weighted ensemble of transformer-based encoder models and a large language model…

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