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Latest Publications & Patents on Small Language Models (SLMs)

Small Language Models

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Small language models
Small language models enable efficient natural language processing on consumer and embedded devices.

Small language models designate transformer-based natural language processing systems operating below approximately 7 billion parameters — a threshold defined less by a formal boundary than by the practical constraint of deployability on consumer hardware, mobile devices, and embedded systems without cloud inference infrastructure.

The domain emerged as a direct response to the computational and economic costs of frontier-scale models: while billion-parameter-plus architectures demonstrate broad general capability, their memory footprint, inference latency, and energy consumption make them structurally incompatible with on-device deployment, privacy-sensitive applications, and low-bandwidth or offline operational contexts.

The central research program is closing the capability gap between compact and frontier models through a combination of knowledge distillation — training a smaller student model against the output distributions of a larger teacher — structured and unstructured pruning, aggressive weight quantization down to INT4 and INT8 representations, and parameter-efficient fine-tuning methods such as LoRA and QLoRA that adapt a compressed base model to domain-specific tasks at minimal additional compute cost.

The publications and patents indexed below address model compression techniques, quantization algorithms, distillation protocols, efficient transformer architectures, on-device inference optimization, and domain-specific fine-tuning pipelines:

This is our latest selection of worldwide publications and patents in english on Small Language Models (SLMs), between many scientific online journals, classified and focused on small language model, SLM, on-device language model, edge language model, compact transformer, sub-7B parameter model, language model compression, knowledge distillation NLP, structured pruning language model, unstructured pruning language model, weight quantization language model, INT4 quantization NLP, INT8 quantization NLP, parameter-efficient fine-tuning, LoRA fine-tuning, QLoRA fine-tuning, adapter tuning language model, on-device inference, edge inference NLP, speculative decoding, model distillation transformer, GGUF quantization format and mixture-of-experts compact model.

Parameter-free method for efficient and accurate llm inference acceleration via speculative decoding

Patent published on the 2026-05-07 in WO under Ref WO2026092843 by MARZOLLO MICHELE [DE] (Marzollo Michele [de], Mueller Lorenz [de], Zhuang Jiawei [de], Roemer Niklas [de], Cavigelli Lukas [de])

Abstract: In some examples, apparatus and methods are provided for selecting a draft token sequence for verification by using a large language model, LLM. Different sources of statistics on text data (prompt, generated output, large dataset of text data) can be utilized in order to choose candidates to use for speculative decoding via look-ups.[...]


Our summary: This method accelerates LLM inference without parameters by using speculative decoding. It selects draft token sequences for verification through statistical analysis of text data. The approach utilizes various sources of statistics to optimize candidate selection for decoding.

speculative decoding, LLM inference, token sequence selection, text data statistics

Patent

Automated synthesis of planar linkage mechanisms with diverse joint types via spring-connected link models and contrastive graph learning

Published on 2026-03-28 by @OXFORD

Abstract: AbstractThe automated synthesis of planar linkage mechanisms has long been a challenge in mechanism design, requiring both geometric feasibility and motion accuracy. Recent advances in data-driven and neural network–based methods have shown promise in automating linkage synthesis, improving efficiency and scalability compared to traditional analytical or optimization-based techniques. Nevertheless, existing data-driven approaches remain limited in handling diverse joint configurations and ofte[...]


Our summary: This study presents a framework for automating the synthesis of planar linkage mechanisms using deep learning and physics-based modeling. It employs a spring-connected link model for diverse joint configurations and utilizes contrastive graph learning for efficient linkage retrieval. The method demonstrates improved accuracy and optimization stability compared to traditional approaches.

mechanism synthesis, deep learning, contrastive graph learning, optimization stability

Publication

Enhancing Whisper Fine-Tuning with Discrete Wavelet Transform-Based LoRA Initialization

Published on 2026-01-29 by Liang Lan, Molin Fang, Yuxuan Chen, Daliang Wang, Wenyong Wang @MDPI

Abstract: In low-resource automatic speech recognition (ASR) scenarios, parameter-efficient fine-tuning (PEFT) has become a crucial approach for adapting large pre-trained speech models. Although low-rank adaptation (LoRA) offers clear advantages in efficiency, stability, and deployment friendliness, its performance remains constrained because random initialization fails to capture the time&amp;ndash;frequency structural characteristics of speech signals. To address this limitation, this work proposes[...]


Our summary: This work introduces a structured initialization mechanism combining LoRA with discrete wavelet transform for fine-tuning in low-resource ASR. The proposed DWTLoRA method enhances convergence speed, stability, and accuracy by aligning with speech signal characteristics. Experimental results show DWTLoRA outperforms standard LoRA and other PEFT methods in character error rate and training efficiency.

Fine-Tuning, Discrete Wavelet Transform, Low-Rank Adaptation, Automatic Speech Recognition

Publication

A Systematic Evaluation and Adaptation of Large Language Models for Very Short-Term Power Load Forecasting

Published on 2026-01-26 by Yansheng Chen, Miao Chen, Chenchao Hu, Jinxi Wu, Ruilin Qin @MDPI

Abstract: Power load forecasting is critical for ensuring grid security and stability and optimizing energy resource allocation. The high integration of renewable energy poses significant challenges to traditional methods in data-scarce scenarios. Recently, Large Language Models (LLMs) have shown considerable potential in processing time-series data, yet their effectiveness in very short-term power load forecasting lacks systematic evaluation. This paper proposes a targeted prompt engineering framework an[...]


Our summary: This study evaluates the effectiveness of Large Language Models (LLMs) for very short-term power load forecasting. It proposes a targeted prompt engineering framework and introduces Ele-LLM, a model utilizing Low-Rank Adaptation for improved performance. Experimental results indicate Ele-LLM significantly outperforms traditional forecasting methods, demonstrating the potential of LLMs in this domain.

Large Language Models, Power Load Forecasting, Prompt Engineering, Low-Rank Adaptation

Publication

A Comparative Study and Introduction of a New Heat Source Model for the Macro-Scale Numerical Simulation of Selective Laser Melting Technology

Published on 2026-01-25 by Hao Zhang, Shuai Wang, Junjie Wang, Zhiqiang Yan @MDPI

Abstract: Selective Laser Melting (SLM), as a common metal additive manufacturing (AM) technology, achieves high-precision complex part formation by layer-by-layer melting of metal powder using a laser. However, the dynamic behavior of the melt pool during the SLM process is influenced by the heat source model, which is crucial for suppressing porosity defects and optimizing process parameters, directly determining the reliability of numerical simulations. To address the issue of traditional surface heat [...]


Our summary: This study introduces a new dynamic heat source model for Selective Laser Melting (SLM) to improve numerical simulations. It compares various heat source models and demonstrates that the dynamic model achieves greater accuracy in predicting melt pool dimensions. The results indicate significant improvements in both melt pool width and depth compared to traditional models.

Heat Source Model, Selective Laser Melting, Finite Element Analysis, Temperature Field

Publication

Influence and Optimization of Process Parameters on Surface Roughness of Selective Laser Melting of 316L Stainless Steel

Published on 2026-01-20 by Pin Dong, Kamonpong Jamkamon, Suppawat Chuvaree @MDPI

Abstract: To achieve better surface quality in selective laser melting (SLM), this study used 316L stainless steel powder and conducted a systematic design experiment to investigate the influence mechanism of process parameters on the surface roughness of the top and vertical surfaces. Response surface methodology (RSM) was then used for parameter optimization. The results showed that scanning speed has the greatest impact on surface roughness, followed by laser power, while scanning spacing has the least[...]


Our summary: This study investigates the impact of process parameters on the surface roughness of 316L stainless steel in selective laser melting. Scanning speed significantly affects surface quality, with optimal conditions identified for minimal roughness. The findings validate the effectiveness of the response surface methodology used for parameter optimization.

Selective Laser Melting, Surface Roughness, Process Parameters, Response Surface Methodology

Publication

Effect of Selectively Etched Al-Rich and Si-Rich Microstructures on the Adhesion of Polyimide Coatings to SLM AlSi10Mg

Published on 2026-01-18 by Jianzhu Li, Shuo Yang, Yujie Li @MDPI

Abstract: Interfacial adhesion between selective laser-melted (SLM) AlSi10Mg and polyimide (PI) insulating coatings is often limited by mismatched physicochemical properties. To improve adhesion, Al-rich and Si-rich microstructured surfaces were fabricated on the XY plane (perpendicular to the build direction) and the Z plane (parallel to the build direction) by acidic and alkaline etching, exploiting the characteristic microstructure of SLM AlSi10Mg. Surface topography, chemical composition, and wettabil[...]


Our summary: Selectively etched microstructures on AlSi10Mg improve adhesion to polyimide coatings. Al-rich surfaces showed cohesive failure, while Si-rich surfaces exhibited mixed failure modes. Shear and pull-off tests demonstrated significant increases in mechanical performance compared to polished surfaces.

adhesion, microstructures, polyimide, selective laser melting

Publication

Monolithic microelectromechanical systems based spatial light modulators including multiple arrays, each array configured to modulate different wavele

Patent published on the 2026-01-02 in WO under Ref WO2026006343 by SILICON LIGHT MACHINES CORP [US] (Hamann Stephen [us], Payne Alexander [us], Hunter James [us], Liu Tianbo [us], Eng Lars [us])

Abstract: A monolithic spatial light modulator (SLM) is provided. Generally, the SLM includes a substrate with a number of substrate electrodes in a surface thereof, multiple MEMS-based linear arrays formed on the surface of the substrate, and a drive circuit monolithically integrated in the substrate below the surface of the substrate. Each linear array includes multiple ribbons suspended above the surface of the substrate, each ribbon having a light reflective surface facing away from the surface of the[...]


Our summary: The content describes a monolithic spatial light modulator (SLM) that includes multiple MEMS-based linear arrays. Each array is designed to modulate different, non-overlapping wavelength ranges. The SLM integrates a drive circuit with substrate electrodes to control the electrostatically displaceable ribbons for light modulation.

MEMS, spatial light modulators, wave modulation, monolithic integration

Patent

Topics covered: Small Language Models, Natural Language Processing, Transformer-based Systems, Parameter Efficiency, Knowledge Distillation, Model Compression, Structured Pruning, Unstructured Pruning, Weight Quantization, INT4, INT8, Fine-tuning Methods, On-device Deployment, Inference Latency, Energy Consumption, Privacy-sensitive Applications, Low-bandwidth Operations, Offline Operational Contexts, IEEE 80211, ISO/IEC 30170, ISO/IEC 27001, ISO/IEC 25010, and NIST SP 800-53..

Glossary of Terms Used

Natural Language Processing (NLP): a field of artificial intelligence focused on the interaction between computers and human language, enabling machines to understand, interpret, and generate natural language text or speech. It encompasses tasks such as language translation, sentiment analysis, and speech recognition.

Small Language Models (SLM): compact neural networks designed for natural language processing tasks, typically characterized by fewer parameters and reduced computational requirements compared to larger models, while still capable of generating coherent text and understanding context within limited scopes.

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