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natural language processing

9,568 papers

#natural language process... Preprint Oct 2026

Label Collapse in Diffusion Language Model In-Context Learning

A systematic evaluation of ICL failure modes is presented, complementing contemporaneous studies of dLLM context comprehension and demonstration-order sensitivity, and open questions about how iterative denoising interacts with label biases are identified.

Zi-Ye Guo, Ye Yuan · 0 citations
#natural language process... Preprint Open access Oct 2026

The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language?

Text generated by Large Language Models (LLMs) often remains limited in diversity compared to that produced by humans. Recent advances demonstrate the presence of inter- and intra-model collapse in open-ended generation of English text, raising concerns about the long-term homogenization of LLM, and more importantly, h...

Xaver Raphael Davey, Shubhra Mishra, Anton Adel\"ow et al. · 0 citations
#computer vision Preprint Oct 2026

Lost in Contrast: Instruction-Preserving Decoding for Reliable Visual Question Answering

Visual contrastive decoding corrects answers using the prediction difference between the original image and a reference condition, yet the same contrast also turns supported abstentions into incorrect answers. We find that abstentions are lost at two stages: contrastive decoding with candidate filtering can exclude the...

Hao-Nan Zhang, Ting Jin, Wen-Zhong Tang et al. · 0 citations
#natural language process... Preprint Oct 2026

What Is the"Self"of a Large Language Model?

What could the"self"of a large language model be, if it is neither a particular word nor a single privileged layer? We propose a relational account: a functional self is an actor-centered organization of information that distinguishes the model's recorded actions from externally supplied material while content and reas...

Yu-Chen Li, Zong-Qi Fan, Xin-Tong Li et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

EHR-Bench: A Chinese Multi-Task Benchmark for Recruitment AI

Recruitment applications span diverse operations, from drafting job descriptions and interpreting queries to organizing profiles, assessing fit, and recommending candidates and jobs. Systematic LLM evaluation therefore requires task coverage that reflects this range of business needs. We introduce EHR-Bench, an open Ch...

Ruijia Yang, Bing Xu, Zulong Chen et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

SIREN: Situation-aware Index for Response and Emergency ActioNability in Crisis Translation

Machine translation (MT) is increasingly utilized in multilingual crisis communication, yet conventional evaluation does not directly assess whether translated emergency messages preserve their actionability. We introduce SIREN (Situation-aware Index for Response and Emergency ActioNability), a human evaluation framewo...

Maria Carmen Staiano, Lifeng Han, Johanna Monti et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection

Model-internal signals, such as neuron activations and gradients, have been used for instruction data selection, yet how neuron relevance should be characterized remains underexplored. In particular, should selection prioritize the magnitude of relevance or how broadly it is distributed across neurons? To investigate t...

Yifan Le · 0 citations
#natural language process... Preprint Oct 2026

Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models

Enter intent-triggered anchoring, a training-free decoding rule that completes a format unit once the model commits its first token, inserting only units with a unique, content-free completion; fallbacks write the unit when the model stops or loops without starting it.

Junhyeok Lee, Kyu Sung Choi · 0 citations
#natural language process... Preprint Open access Oct 2026

Communication, Vocalisation, and Language

The contention is that the current batch of Large Language Models don't *understand* language, but they *do* it. Rather than LLMs forming some kind of ``distributed representation'' of symbols, the claim is that LLMs are capturing ``Cultural Intelligence'' and grounding symbols in what the sociologists call practices.

Peter Wallis · 0 citations

Ontology-Based Post-Coordinated Expression Assembly: from Clinical Narrative to Queryable Knowledge Graphs

Clinical oncology documentation records decisive information in free text, where it remains readable but not retrievable at scale, leaving cohort identification dependent on incomplete diagnostic codes or manual review. The current standard, coding individual terms, captures what a document mentions, not what it assert...

Johann Pignat, C. Gaudet-Blavignac, Julien Ehrsam et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

The interpreter as developer: An autoethnographic design study of building a computer-assisted interpreting tool through human-LLM co-creation

Real-time AI captioning now serves audiences at multilingual events, yet computer-assisted interpreting (CAI) tools are still built for interpreters, rarely by them. This article reports an autoethnographic design study in which a professional interpreter with no programming training built a local-first CAI system thro...

Zeming Zhao (College of Foreign Languages, Capital Normal University, Beijing et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

CivWorldBench: Evaluating Language World Models in Structured Multi-Agent Sandbox Worlds

The growing use of language models for forecasting motivates a broader evaluation of their world-model capabilities. Such evaluation must go beyond predictive accuracy to assess hidden-state inference and intervention prediction. Real-world forecasting benchmarks offer limited access to hidden-state ground truth and co...

Ang Li, Haofei Yu, Zhengwei Tao 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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