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最新の出版物:小型言語モデル(SLM)に関する特許

小型言語モデル

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Small language models
小さな言語モデルは効率的な 自然言語処理 民生機器および組み込み機器向け。

小規模言語モデルとは、約70億パラメータ未満で動作するトランスフォーマーベースの自然言語処理システムを指します。この閾値は、形式的な境界というよりも、クラウド推論インフラストラクチャを持たない消費者向けハードウェア、モバイルデバイス、組み込みシステムへの展開可能性という実際的な制約によって定義されます。

この分野は、最先端規模のモデルにおける計算コストと経済コストへの直接的な対応として出現しました。10億個以上のパラメータを持つアーキテクチャは幅広い汎用性を示す一方で、そのメモリ使用量、推論遅延、およびエネルギー消費量により、デバイス上での展開、プライバシーに配慮したアプリケーション、低帯域幅またはオフラインの運用環境とは構造的に互換性がありません。

中心となる研究プログラムは、知識蒸留(より大きな教師の出力分布に対してより小さな生徒モデルを訓練する)、構造化および非構造化剪定、INT4およびINT8表現への積極的な重み量子化、そして圧縮された基本モデルを最小限の追加計算コストでドメイン固有のタスクに適応させるLoRAやQLoRAなどのパラメータ効率の高い微調整手法を組み合わせることで、コンパクトモデルと最先端モデルの間の能力ギャップを埋めています。

以下に索引付けされた出版物および特許は、モデル圧縮技術、量子化アルゴリズム、蒸留プロトコル、効率的なトランスフォーマーアーキテクチャ、デバイス上での推論最適化、およびドメイン固有の微調整パイプラインを扱っています。

これは、多数の科学オンラインジャーナルの中から、小型言語モデル (SLM) に関する英語の世界中の出版物と特許の最新のセレクションです。小型言語モデル、SLM、オンデバイス言語モデル、エッジ言語モデル、コンパクトトランスフォーマー、7B 未満のパラメータモデル、言語モデル圧縮、知識蒸留 NLP、構造化剪定言語モデル、非構造化剪定言語モデル、重み量子化言語モデル、INT4 量子化 NLP、INT8 量子化 NLP、パラメータ効率の高い微調整、LoRA 微調整、QLoRA 微調整、アダプタチューニング言語モデル、オンデバイス推論、エッジ推論 NLP、投機的デコード、モデル蒸留トランスフォーマー、GGUF 量子化フォーマット、および専門家の混合コンパクトモデルに分類され、焦点を絞っています。

Deformable high-strength aluminum alloy compositions and methods of making the same

Patent published on the 2026-06-04 in US under Ref US20260152827 by PURDUE RES FOUNDATION [US] (Zhang Xinghang [us], Wang Haiyan [us], Stegman Benjamin Thomas [us], Shang Anyu [us])

Abstract: [0000] An alloy comprising 92 at % aluminum, 2 at % titanium, 2 at % iron, 2 at % cobalt, and 2 at % nickel. A method of making an alloy is disclosed. The method contains the steps of providing particles of desired composition, utilizing a selective leaser melting (SLM) apparatus producing a first layer of the particles on a substrate and melting and solidifying a first group selected areas of the layer of particles, wherein the melting and the solidification results in an alloy of desired compo[...]


Our summary: The content describes a high-strength aluminum alloy with specific composition percentages. It outlines a method for creating the alloy using selective laser melting to achieve desired thickness and shape. The process involves layering particles, melting, and solidifying selected areas to form intermetallic structures.

aluminum alloy, selective laser melting, intermetallic lamellae, high-strength

Patent

Quantization-aware lora fine-tuning for llm

Patent published on the 2026-06-04 in US under Ref US20260154540 by MEDIATEK SINGAPORE PTE LTD [SG] (Lim Jia Yao Christopher [sg], Huang Ya-lin [tw], Li Huai-ting [tw], Wong Wai Mun [sg], Liang Jen-wei [tw], Lee Timothy Jun Jie [sg])

Abstract: [0000] In an aspect of the disclosure, a method of using a LoRA for inference with a FC layer of a LLM is provided. The method includes: dequantizing an INT input to an FP output; processing the FP output from the DQ and a first FP input from first weights of a down projection module of the LoRA, to output a first FP output; processing the first FP output from the first BMM and a second FP input from second weights of an up projection module of the LoRA, to output a second FP output; quantizing [...]


Our summary: The method describes using LoRA for inference in a fully connected layer of a large language model. It involves dequantizing inputs, processing them through down and up projection modules, and quantizing outputs. The final output is an INT inference result derived from the LoRA adjustments.

Quantization, LoRA, fine-tuning, LLM

Patent

Systems and methods for assisting operation and maintenance of marine machine equipment

Patent published on the 2026-06-03 in EP under Ref EP4752805 by ALFA LAVAL CORP AB [SE] (Karlsson Jimmie [se], Boman Jesper [se])

Abstract: [0001] The present invention relates to a method of operating and maintaining a piece of marine machine equipment. The piece of marine machine equipment is connected to a local processor. The method comprising the steps of obtaining a set of training data specific to the piece of marine machine equipment and training a Small Language Model (SLM) with the set of training data specific to the piece of marine machine equipment. The method further comprising the step of executing the trained SLM on [...]


Our summary: The invention describes a method for operating and maintaining marine machine equipment using a local processor. It involves training a Small Language Model (SLM) with specific training data for the equipment. The trained SLM provides offline operational advice utilizing real-time data from the equipment.

marine machine equipment, operational advice, Small Language Model, real-time data

Patent

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

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

A Lightweight LLM-Based Semantic&ndash;Spatial Inference Framework for Fine-Grained Urban POI Analysis

Published on 2026-01-16 by Zhuo Huang, Yixing Guo, Shuo Huang, Miaoxi Zhao @MDPI

Abstract: Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic&amp;ndash;spatial inference framework (LLM-SSIF), a lightweight semantic&amp;ndash;spatial pipeline that translates POI texts into interpretable, fine-grained spatial evidence through an end-to-end workflow that couples scalable label expansion with scale-controlled sp[...]


Our summary: This study introduces LLM-SSIF, a lightweight framework for translating unstructured POI texts into spatial evidence. It employs LoRA-based fine-tuning for efficient adaptation and enhances label coverage. The model demonstrates strong performance in urban analysis, revealing cultural differences between cities.

LLM, semantic inference, spatial analysis, fine-grained POI

Publication

取り上げるトピック: 小規模言語モデル、自然言語処理、Transformer ベースシステム、パラメータ効率、知識蒸留、モデル圧縮、構造化枝刈り、非構造化枝刈り、重み量子化、INT4、INT8、微調整手法、デバイス内展開、推論遅延、エネルギー消費、プライバシーに敏感なアプリケーション、低帯域幅操作、オフライン操作コンテキスト、IEEE 80211、ISO/IEC 30170、ISO/IEC 27001、ISO/IEC 25010、および NIST SP 800-53。

用語集

Natural Language Processing (NLP): 人工知能の一分野であり、コンピュータと人間の言語との相互作用に焦点を当て、機械が自然言語のテキストや音声を理解、解釈、生成することを可能にする。言語翻訳、感情分析、音声認識などのタスクが含まれる。

Small Language Models (SLM): 自然言語処理タスク向けに設計されたコンパクトなニューラルネットワークは、一般的に、より大規模なモデルと比較してパラメータ数が少なく、計算要件も軽減されているという特徴を持ちながら、限られた範囲内で一貫性のあるテキストを生成し、文脈を理解する能力を備えている。

歴史的背景

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(日付が不明または関連性がない場合、例えば「流体力学」などでは、その注目すべき出現時期の概算値が提示されます。)

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