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natural language processing

9,568 papers

#natural language process... Preprint Oct 2026

zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss

Dynamic vocabularies built on-the-fly via Lempel-Ziv-Welch (LZW) compression let a language model represent the same text in fewer tokens, but prior work shows that this efficiency comes at a cost to reasoning and perplexity. We show that this quality gap is not a fundamental price of compression but can be substantial...

Sai-Bo Geng, A. Gentilini, Xin Ma et al. · 0 citations
#natural language process... Preprint Oct 2026

Measuring Artifact Reliance in Machine-Generated Text Detectors

Detectors of machine-generated text score almost perfectly on scientific-literature benchmarks, implying we can reliably distinguish with these detectors human and machine-generated documents. We looked at ten scientific-document subsets from six popular resources, and on several of them that apparent ease is a side ef...

Anastasia Voznyuk, Andrey Grabovoy · 0 citations
#natural language process... Preprint Open access Oct 2026

Personalization Without Stereotyping: Do Speech LLMs Know When to Turn a Deaf Ear?

Recent speech LLMs can leverage rich paralinguistic cues, enabling more complex and personalized interactions. However, little work investigates how this sensitivity can lead to stereotyping, and existing evaluations largely measure demographic disparities without distinguishing beneficial personalization from stereoty...

Kshitij Ambilduke, Caroline Brun, Thibaut Thonet et al. · 0 citations
#natural language process... Preprint Oct 2026

Proactive Agentic Orchestration

Agentic orchestration delegates tasks to sub-agents with independent contexts, providing a key path to extending the effective execution horizon of large language model agents. However, existing orchestration methods face two limitations: in execution, current methods fix sub-agents'goals and harnesses at creation time...

Xue-Yang Feng, Chao-Zhuo Li, Xiao-He Bo et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

A Better Metric Will Not Save You: What You Compare Beats How You Compare It

Representational similarity measures (RSA, CKA, Procrustes) are chosen for the transformations they treat as irrelevant, and all of them take the set of conditions as given. That set is fixed by two decisions, which inputs are admitted and how they are grouped, both taken before the matrix a measure operates on exists:...

Noureddine Khaous (LLF - UMR7110, UPCit\'e), Guillaume Wisniewski (LLF - UMR7110 et al. · 0 citations
#natural language process... Book Open access Oct 2026

Towards Enriching Design-Time Models with Evidence from Simulation and Operational Activities

Cyber-Physical Systems are developed through multiple model-based activities, comprising several stages of development. As the development progresses through simulation, experimentation, and operation, observations of system execution may reveal missing, uncertain, or invalid design knowledge. The path from execution r...

Barbara da Silva Oliveira, Julien Deantoni, Nicolas Ferry · 0 citations

Towards the Roof of the World: NLP Progress in Tibetan Languages

Tibetan, spoken by some seven million people on and around the Qinghai-Xizang Plateau, remains underrepresented in natural language processing (NLP). We present, to our knowledge, the first peer-reviewed systematic bibliometric survey of Tibetan NLP: 140 publications from 2004 to 2026 in the ACL Anthology and DBLP, ann...

Xi Cao, Yi-Jie Li, Yuan Sun et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

MoPrO-SD: Modular Prompt Optimization for Stance Detection

Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Although automatic prompt optimization offers an alternative to fine-tuning without updating model parameters, existing structured approaches are not specifically designe...

Meysam Sabbaghan, Nikesh Gyawali, Arman Zareian Jahromi et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

From Structure to Validity: Mechanism-Guided Generation and Refinement of LLM Rubrics

Rubrics have been widely used to evaluate and optimize language models on open-ended tasks. However, existing work primarily focuses on generating better rubrics, with limited understanding of what makes rubric design effective. In this work, we conduct a systematic mechanistic analysis of rubric design across criterio...

Feifei Qian, Xuan Chen, Lixin Cui et al. · 0 citations
#natural language process... Preprint Oct 2026

Test-Time Memory Construction

Large Language Models (LLMs) struggle to answer questions over long interaction histories, where relevant evidence may be scattered across distant sessions. Existing memory systems typically construct and maintain sophisticated memory representations offline, before user queries are observed, incurring substantial over...

Zi-Jun Wang, Yi-Fan Ji, Haidong Xin et al. · 0 citations
#natural language process... Preprint Oct 2026

Host-Aware Web Crawling for LLM Pretraining Data

Web crawling is a major source of large language model (LLM) pretraining data, yet data-selection pipelines discard a large share of crawled pages. We investigate how a crawler should allocate its budget across and within hosts to collect more high-quality pretraining data. Combining document-level quality classifier s...

Malte Ostendorff · 0 citations
#natural language process... Preprint Open access Oct 2026

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive patterns. In this work, we focus on low resource fake news detection, where only a small labeled set is available for training, while evaluation is performed under a fixed and...

Ziyu Luo, Wei Li, Haoping Huang et al. · 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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