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natural language processing

9,568 papers

#natural language process... Preprint Sep 2026

Automated Evaluation of Multi-Turn Dialogues in In-Car Conversational Assistants

In-car conversational assistants (ICAs) are increasingly integrated into vehicles to support route planning, vehicle control, and information access. Ensuring their reliability is challenging due to multi-turn interactions, the absence of explicit ground truth, and strict safety constraints. Existing evaluation techniq...

Vaishnav Negi, Lev Sorokin, S. T. Arasteh et al. · 0 citations
#natural language process... Preprint Open access Sep 2026

Developing an OCR model for Extracting Information from Invoices with Korean Language

Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Making the extraction of important information crucial. The stored information serves different purposes. Korean language is the native language of about 80 million people, playing an impo...

Xiem HoangVan, Phu TranQuang, Minh DinhBao et al. · 0 citations

FD-VAD: Semantic Endpoint Detection for Streaming Full-Duplex Speech

DF-VAD is introduced, an ASR-free streaming endpointer that maps bounded causal audio windows directly to Continue/Stop decisions and outperforms strong streaming and non-streaming semantic turn classifiers, and achieves the highest EOT recall among qualifying systems on TurnBench dev set $0.853.

Puneet Mathur, Dinesh Manocha · 0 citations
#machine learning Preprint Open access Sep 2026

C-Instrument: Automating RL Data Generation and Hillclimbing with a Constitution-Grid Instrument

Conflicting objectives are general in RL alignment, and training on them data-efficiently is hard. Training a safety guard with RL means optimizing two objectives that conflict: catch real harm, and do not refuse benign prompts. Our finding is that over-refusal improves 22.4% to 12.8%, while under-refusal on adversaria...

Xianling Zhang · 0 citations
#machine learning Preprint Open access Sep 2026

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval augmented generation (RAG) tries to reduce factual errors by identifying information in a knowledge corpus and putting it in the context win...

Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo et al. · 0 citations
#machine learning Preprint Open access Sep 2026

Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems

Heterogeneous multi-agent systems combine models with different capabilities through a common communication interface. Exchanging internal states directly requires translating between model-specific representations and controlling intermediate computation. We introduce the Vision Wormhole, which repurposes the visual i...

Xiaoze Liu, Ruowang Zhang, Weichen Yu et al. · 0 citations
#machine learning Preprint Open access Sep 2026

Relative Kinetic Utility: Calibrating Cross-Layer Credit for Global Structured LLM Pruning

Global structured pruning requires channels from different layers to compete under a shared sparsity budget, raising two coupled challenges: identifying which channels should be retained and making their scores comparable across layers. Raw channel scores can contain block-common scale that leaves within-block ordering...

Tianhao Qian, Guilin Qi, Jiayu Chen · 0 citations
#machine learning Preprint Open access Sep 2026

Block Sparse Flash Attention

Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity creates a severe computational bottleneck. We present Block Sparse Flash Attention (BSFA), a drop-in replacement that accelerates long-context inference while preserving model qua...

Daniel Ohayon, Itay Lamprecht, Itay Hubara et al. · 0 citations
#machine learning Preprint Open access Sep 2026

MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection

In recent years, Sentiment Analysis (SA) and Emotion Recognition (ER) have been increasingly popular in the Bangla language, which is the seventh most spoken language throughout the entire world. However, the language is structurally complicated, which makes this field arduous to extract emotions in an accurate manner....

Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi et al. · 0 citations
#machine learning Preprint Sep 2026

Learning What to Remember: Long-horizon Counterfactual Memory Optimization

Persistent textual memory allows language models to carry information across long interactions, but learning what to remember is fundamentally a credit-assignment problem. A memory rewrite may only become useful many steps later, while much of the observed utility may be inherited from information already stored before...

Jiaming Tang, Ming-Yan Liu, Armin Sarabi · 0 citations
#machine learning Preprint Sep 2026

It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs

SYNTH is the first open-source synthetic corpus derived from 58,698 Wikipedia articles that collapses pre, mid, and post-training into a single training stage via structured amplification of curated encyclopedic seeds, and opens up possibilities for both generalist models with significantly increased data efficiency, a...

Pierre-Carl Langlais, Pieter Delobelle, Yannick Detrois et al. · 0 citations
#machine learning Preprint Sep 2026

How Many Labels Does a Language Need? Annotation Budgets and Cross-Lingual Pooling for African-Language Text Classification

Every text classifier for an African language begins with a budgeting question: how many labelled examples are needed, and can labels from other African languages stand in for them? We answer both questions empirically for 28 language-task pairs, news topic classification in 16 languages (MasakhaNEWS) and tweet sentime...

Bhanu Prakash Vangala, S. Guda, Navya Vangala · 0 citations

From tech blogs

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Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.

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