CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods
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Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
TeamFusion uses per-member proxy agents and iterative structured discussions to generate more representative and consensual team deliverables than direct aggregation in open-ended tasks.
Presents TR-EduVSum dataset and AutoMUP consensus framework for generating gold-standard summaries from multiple human annotations of Turkish educational videos.
ScaLoRA analytically derives per-update column scalings that let low-rank increments accumulate into high-rank weight updates, yielding faster convergence and higher accuracy than prior LoRA variants on LLMs up to 12B parameters.
LaMSUM is a novel multi-level LLM framework with voting methods for extractive summarization of large incident report collections that outperforms prior extractive methods.
MAGIC-HMO is a multi-agent framework that treats Chinese short-form creative NLG as heterogeneous multi-objective optimization over personalized constraints plus explanation reliability and outperforms baselines on a baby-naming benchmark.
ARIEL evaluates LLMs and LMMs on full-length biomedical summarization and figure interpretation with blinded expert review, identifies limitations, and demonstrates gains from prompt engineering, fine-tuning, and an integrated agent for hypothesis generation.
The work introduces WaLeF/FIDLAr for flood forecasting, CoDiCast for probabilistic weather, and Hypercube-RAG for explainable environmental QA, claiming superior accuracy, efficiency, and interpretability over baselines.
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