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small language model

3,366 papers

#large language models Open access Aug 2026

What Changed the Exponent of a Chain Was Self-Avoidance Alone, and above Four Dimensions That Cost Disappears ── An Error of 2.11% in the Exponent Becomes 29.33% in the Length at N=10^9 ── And 2+2=4 Empties Avoidance of Its Meaning ── [Paper 279]

The spread of a polymer chain is fixed by a power of the number of units N. For a Gaussian chain it is N^1/2, and for a self-avoiding chain N^0.588. This paper asks where that difference comes from and where it disappears──the answer is the count 2+2=4. No new mathematical theorem and no new law is claimed. Scope of th...

Yuuki Yamagishi · 0 citations
#large language models Dataset Open access Aug 2026

Police accountability data: United Kingdom, United States and Australia

Derived, machine-readable datasets on police complaints, stop and search, use of force and deaths following police contact, for the United Kingdom, the United States and Australia. Every figure is computed from official open data by code and published with its source, its period and its denominator attached. No figure...

PoliceComplaint.com · 0 citations
#small language model Open access Aug 2026

PARA: Perception, Action, Reasoning, Adaptation. Four Faculties an Institution Can Revoke

The fourth faculty is Adaptation. Any source rendering it as Reflection is in error, including sources by this author, and the distinction is not cosmetic: reflection is a private act with no external consequence, while adaptation writes to institutional memory, which is why it needs a guardrail and why misnaming it re...

Nabeel A. Khan · 2 citations
#large language models Open access Aug 2026

Fine-Tuning a Large Multilingual Text-to-Speech Model for Nepali on Consumer Hardware: A Teacher Model for Knowledge Distillation with Speaker Identity Verification

Nepali is a low resource language for speech technology and there is very little open text-to-speech support for it. Most high quality neural TTS models are too large to run in real time on the low end machines that are common in Nepal, and the usual answer to that problem is knowledge distillation, where a large teach...

Yagya Raj Sharma · 0 citations
#small language model Review Open access Aug 2026

Self-reported AI literacy and AI anxiety as correlates of AI attitudes among speech-language therapy students in Türkiye: a cross-sectional study

These findings describe concurrent adjusted associations rather than temporal or causal pathways and should be interpreted in light of the weak measurement structure of the AI-literacy instrument in this sample.

Şaziye Seçkin Yılmaz, N. Birol · 0 citations
#large language models Open access Aug 2026

Fine-Tuning a Large Multilingual Text-to-Speech Model for Nepali on Consumer Hardware: A Teacher Model for Knowledge Distillation with Speaker Identity Verification

Nepali is a low resource language for speech technology and there is very little open text-to-speech support for it. Most high quality neural TTS models are too large to run in real time on the low end machines that are common in Nepal, and the usual answer to that problem is knowledge distillation, where a large teach...

Yagya Raj Sharma · 0 citations
#natural language process... Preprint Aug 2026

Ladders in Chaos: When, How, (and Perhaps Why) Does Test-Time Scaling Improve LLM Machine Translation

This study shows that sequential sampling has a higher performance ceiling, providing a more diverse and effective pool of samples, particularly under smaller sampling budgets, and suggests an explanation of the mechanism through which sequential scaling improves machine translation.

Di Wu, S. Troshin, C. Monz et al. · 1 citation
#artificial intelligence Review Aug 2026

Learning a Size-Weight Frontier for Synthetic-Augmented Inference

A general framework for synthetic-augmented inference across a population of related tasks is developed, which characterizes synthetic augmentation by the number of synthetic observations and their weight and specifies a size-weight frontier that specifies, for each weight, the largest synthetic sample size for which a...

Chengpiao Huang, Kaizheng Wang · 0 citations
#artificial intelligence Preprint Aug 2026

ARC-CT: Anatomy-Routed Contrastive Vision-Language Learning for 3D Chest CT

An Anatomy-Routed Contrastive Learning for 3D Chest CT (ARC-CT), a region-aware framework that addresses limitations using only reports extracted from reports by an LLM, with no manual annotations or bounding boxes, outperforms both comparable efficient baselines and larger transformer models.

H. Isik, Mehmet Alp Ozaydin, S. Kurugol et al. · 1 citation
#artificial intelligence Preprint Aug 2026

When Linguistic and Internal Confidence Diverge in Large Language Models

Regression analyses show that distributional properties of confidence scores explain much of the observed alignment pattern, with model metadata playing a smaller role after controls, and support a lossy-channel view of linguistic confidence.

Hefan Zhang, Bing-Quan Zhang, Ming Cheng et al. · 2 citations
#natural language process... Preprint Aug 2026

First Make It Playable, Then Make It Good: Staged Interaction Learning for Small Dialogue-Game Agents

It is suggested that imitating full trajectories helps with playability, while turn-level and teacher-guided training usually improve decision-making and increase the overall score, and small models are performant simply by using careful curation strategies rather than aggressive changes.

Syed Mahbubul Huq, P. Madhyastha · 0 citations
#natural language process... Preprint Aug 2026

Below the Noise Floor: Bimodal Seed Collapse and Distinct Failure Modes in Small-Model Knowledge Distillation

On a 740-instance healthcare API routing task with a 1.5B Qwen student and a 20B teacher, eight KD variants are compared against supervised cross-entropy, finding single-seed evaluation is unable to detect central failure modes in small-model KD.

Dipto Sumit, Sakib Ul Haque, Farig Sadeque · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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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