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natural language processing

9,568 papers

#natural language process... Preprint Oct 2026

BeliefScope: Diagnosing Evidence-Driven Revision and Pressure-Induced Shifts in Large Language Models

A language model may revise the same proposition after receiving genuinely relevant evidence or after receiving directional user pressure that adds no relevant fact. The observable response shift alone therefore does not reveal which source drove the change. We introduce BeliefScope, a controlled black-box framework fo...

Shuai-Fei Guo, Yi-Dong Cui · 0 citations
#natural language process... Preprint Oct 2026

When Do We Need On-Policy Distillation? Distilling on Offline Student Rollouts Is Often Better

On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models. In this work, we ask a critical research question: Is on-policy sampling always beneficial for distilling arbitrary teacher-student pairs? We show that a simple alternative, Semi-OPD, which distills fro...

Siyan Zhao, Yong-Gan Fu, Jin-Dong Jiang et al. · 0 citations
#natural language process... Preprint Oct 2026

Local Prototype Reconstruction for Text-Compatible Speech-to-LLM Bridge Pretraining

Speech-to-LLM systems often connect a frozen speech encoder to a frozen large language model (LLM) through a small trainable bridge. The bridge is usually treated as plumbing, but it in fact defines the geometry of the speech-to-LLM interface, and the pretraining objective decides whether that interface provides a reus...

Xin-Nian Zhao, Chia-Hua Wu, Pu Wang et al. · 0 citations
#natural language process... Preprint Oct 2026

The"10th Juror": Open-Set Standpoint Screening for Bureaucratic Bias Detection

Presupposing the boundaries of bias is itself a form of bias. We study closed-loop bias governance for Dutch government documents, where a system must detect biased language, ground decisions in legal and contextual evidence, rewrite problematic sentences when intervention is warranted, and verify that the rewrite miti...

Yu-Chen Miao, Zi-Jun Wang, Chang Han et al. · 0 citations
#natural language process... Preprint Oct 2026

Clinician use of language models diverges from how the models are evaluated

Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchmark scores, most of them derived from examination questions or curated cases. A benchmark predicts performance in deployment only to the extent that its items resemble...

K. Vishwanath, Hai-Tong Lin, A. Alyakin et al. · 0 citations
#natural language process... Preprint Oct 2026

Prompts versus Rules: Auditing and Controlling Speech Naturalness Behaviors in Voice User Simulators

As voice agents gain more popularity commercially, the user simulators used to evaluate the deployed agents are also being developed to include more realistic, variable, and diverse speech naturalness behaviors -- disfluency, interruption and backchanneling. The quality of the user simulator directly affects the validi...

Riqiang Wang, Elena Khasanova, Harsh Saini et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

Back in Style: A Sociolinguistic Approach to Authoring and Measuring Persona Fidelity in User Simulation

As agentic systems gain commercial popularity, user simulators increasingly serve as measurement instrument for their evaluation. However, the fidelity of simulated users in comparison to real human users is generally low, and typically assessed by costly, subjective LLM judges. In this pilot study, we ask whether fide...

Lex Konnelly, Elena Khasanova, Riqiang Wang et al. · 0 citations
#natural language process... Open access Oct 2026

Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged

Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these dimensio...

Hala Almaghout, C. Federmann, Qin Gao · 0 citations
#natural language process... Preprint Oct 2026

Sparse Attention Is Matrix Approximation, Not Choosing from a Bag of Values

It is argued that sparse attention should be formulated as matrix approximation, not as blindly choosing the largest values from a bag of entries, and proposed Matrix Approximation Sparse Attention (MASA), which replaces raw attention-mass ranking with a closed-form score that measures how much each sparse unit reduces...

Fang Wan, Xu-Feng Liu, Fan Li et al. · 0 citations
#natural language process... Preprint Oct 2026

Disentangling Linguistic and Paralinguistic Information with Routed Sparse Autoencoders

Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space. We combine a TopK sparse autoencoder with route-specific supervision and cross-factor adversaries. Across frozen SPEAR and WavLM encoders, independent probes show factor-specific retention and...

Beimnet Bekele Guta, Xiao-Yu Yang, Guang-Zhi Sun et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

Lossy Compressive Text Autoencoders

Our work explores learning a compressed latent representation of text, at the intersection of data compression and representation learning. We propose an autoencoder architecture that performs residual downscaling and upscaling of hidden representations along the time axis, with a residual low-dimension discrete bottle...

Vinko Sabol\v{c}ec, Angelos Katharopoulos, David Grangier · 0 citations
#natural language process... Preprint Open access Oct 2026

Cognitive Thermometers: Machine Learning and Logical Complexity

How does the human mind represent semantic categories? Why do natural languages favor certain meanings over others? Prior explanations have relied on logical definability and complexity, but these are highly sensitive to the choice of logical language, rendering some design choices unmotivated. In this article, we prop...

Shane Steinert-Threlkeld, Jakub Szymanik · 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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