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Dernières publications et brevets sur les petits modèles de langage (SLM)

Modèles de langage de petite taille

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> outil de recherche de publications gratuit < par auteur, sujet, mots-clés, date ou revue.

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Modèles de langage à petite échelle
Les modèles de langage compacts permettent un traitement efficace du langage naturel sur les appareils grand public et embarqués.

Les modèles de langage simplifiés désignent les systèmes de traitement du langage naturel basés sur des transformateurs fonctionnant avec moins de 7 milliards de paramètres environ — un seuil défini moins par une limite formelle que par la contrainte pratique de déploiement sur du matériel grand public, des appareils mobiles et des systèmes embarqués sans infrastructure d'inférence dans le cloud.

Ce domaine est apparu en réponse directe aux coûts de calcul et économiques des modèles à grande échelle : bien que les architectures à plus d'un milliard de paramètres démontrent une large capacité générale, leur empreinte mémoire, leur latence d'inférence et leur consommation d'énergie les rendent structurellement incompatibles avec le déploiement sur appareil, les applications sensibles à la confidentialité et les contextes opérationnels à faible bande passante ou hors ligne.

Le programme de recherche central vise à combler l'écart de capacité entre les modèles compacts et les modèles de pointe grâce à une combinaison de distillation des connaissances — l'entraînement d'un modèle étudiant plus petit par rapport aux distributions de sortie d'un modèle enseignant plus grand —, d'élagage structuré et non structuré, de quantification agressive des poids jusqu'aux représentations INT4 et INT8, et de méthodes de réglage fin efficaces en termes de paramètres telles que LoRA et QLoRA qui adaptent un modèle de base compressé à des tâches spécifiques au domaine à un coût de calcul supplémentaire minimal.

Les publications et brevets indexés ci-dessous traitent des techniques de compression de modèles, des algorithmes de quantification, des protocoles de distillation, des architectures de transformateurs efficaces, de l'optimisation de l'inférence sur l'appareil et des pipelines de réglage fin spécifiques au domaine :

Voici notre dernière sélection de publications et de brevets mondiaux en anglais sur les petits modèles de langage (SLM), parmi de nombreuses revues scientifiques en ligne, classées et axées sur petit modèle de langage, SLM, modèle de langage sur appareil, modèle de langage à la périphérie, transformateur compact, modèle à paramètres sub-7B, compression de modèle de langage, distillation de connaissances NLP, modèle de langage à élagage structuré, modèle de langage à quantification de poids, quantification NLP INT4, quantification NLP INT8, ajustement fin efficace des paramètres, ajustement fin LoRA, QLoRA, modèle linguistique d'élagage non structuré, modèle linguistique de quantification des poids, quantification INT4 NLP, quantification INT8 NLP, réglage fin efficace des paramètres, réglage fin LoRA, réglage fin QLoRA, modèle linguistique d'adaptation, inférence sur appareil, inférence NLP de bord, décodage spéculatif, transformateur de distillation de modèle, format de quantification GGUF et modèle compact de mélange d'experts.

Teaching small language models petro-technical knowledge through language reasoning and fine-tuning

Patent published on the 2026-10-15 in US under Ref US20260319121 by SCHLUMBERGER TECH CORPORATION [US] (Paydayesh Mehdi [gb], Shamsa Amir [ca])

Abstract: A method for automatically generating an answer to a petro-technical query in an oil and gas domain includes receiving input data including question-answer pairs related to the oil and gas domain. The method also includes training a small language model (SLM) based upon the input data to produce a trained model. The method also includes fine-tuning the trained model to produce a fine-tuned model. The method also includes receiving the petro-technical query in the oil and gas domain. The method a[...]


Our summary: This method trains a small language model using question-answer pairs from the oil and gas domain. It fine-tunes the model to improve its ability to generate answers to petro-technical queries. The process enables automatic response generation for specific queries in the oil and gas sector.

small language models, petro-technical knowledge, fine-tuning, query generation

Patent

Method of laser-based additive manufacturing with pulse scan patterns for magnesium and its alloys

Patent published on the 2026-10-08 in US under Ref US20260307150 by OSSEOLABS CO LTD [TH] (Promoppatum Patcharapit [th], Sukpasert Montian [th], Ahuja Vikram [th], Harrysson Ola [us])

Abstract: This invention introduces an advanced pulse scanning technique for Selective Laser Melting (SLM) of magnesium, addressing critical manufacturing challenges. The method employs iterative optimization to regulate heat input through precise pulse patterns, preventing overheating and material defects. By alternating between powered and unpowered laser phases, the technique reduces material evaporation, minimizes internal stresses, and enhances microstructural uniformity. This approach enables more s[...]


Our summary: This invention presents a pulse scanning technique for Selective Laser Melting of magnesium. It optimizes heat input to prevent overheating and defects by alternating laser phases. This method improves fabrication stability and microstructural uniformity for magnesium components.

laser-based manufacturing, pulse scanning, magnesium alloys, Selective Laser Melting

Patent

Memory-efficient neural dialog artificial intelligence

Patent published on the 2026-10-08 in US under Ref US20260310927 by HSU JOSEPH BERNARD [US] (Hsu Joseph Bernard [us])

Abstract: [0000] A hybrid generative AI artificial intelligence system addresses cloud-centric AI inefficiencies through dynamic hierarchical memory indexing and selective cloud escalation. The system implements a three-object architecture comprising user profile management, thread index management, and dialog process control that enables client-side processing with precision-targeted cloud resource utilization. A small language model functions as a local decision engine, performing heuristic inference an[...]


Our summary: A hybrid generative AI system enhances cloud-centric AI efficiency through dynamic memory indexing. It features a three-object architecture for user profile, thread index, and dialog control. The system reduces response time by 84% and cloud dependency by 80% while ensuring cognitive coherence.

neural networks, cloud computing, memory indexing, dialog systems

Patent

Template guided speculative decoding

Patent published on the 2026-10-06 in US under Ref US12754958 by INTUIT INC [US] (Ben Shoham Ofir [il], Bar Eliyahu Natalie [il], Mendelson Shon [il], Antebi Sagiv [il], Ardazi Shai [il])

Abstract: Aspects of the present disclosure involve accelerated machine learning based sequence generation. Aspects include generating a first portion of a sequence based on an input query using a speculative decoding process involving a draft machine learning model and a target machine learning model with a larger number of parameters than the draft model. Aspects include determining, based on the input query and the first portion, that a next portion of the sequence is predicted to correspond to a templ[...]


Our summary: This disclosure involves a method for accelerated sequence generation using speculative decoding with two machine learning models. It includes generating a sequence portion based on an input query and determining if the next portion corresponds to a template. The next portion is generated through a template-based process and verified using a larger target model before appending to the sequence.

speculative decoding, sequence generation, machine learning models, template-based process

Patent

Secure explanation generation by large language models for sensitive search targets

Patent published on the 2026-10-06 in US under Ref US12755173 by INTUIT INC [US] (Eliyahu Natalie Bar [il], Mendelson Shon [il], Hazan Itay [il], Bechler Sigalit [il], Haas Noa [il], Bitton Ron [il])

Abstract: [0000] A method includes generating, by a large language model (LLM), a multitude of match explanations for a multitude of search targets. The multitude of match explanations is clustered to obtain a multitude of explanation clusters. An explanation template is generated for an explanation cluster. The explanation template includes a static portion and a dynamic portion. A multitude of explanation templates is obtained in this manner. A target match for a search target received from a user appli[...]


Our summary: The method generates match explanations for search targets using a large language model. These explanations are clustered to create templates with static and dynamic portions. A small language model populates match explanations based on user search targets and encoded templates.

explanation generation, large language model, sensitive search targets, clustering

Patent

Speculative decoding using expanded token matching

Patent published on the 2026-09-03 in US under Ref US20260260065 by MICROSOFT TECH LICENSING LLC [US] (Pathak Sayan Dev [us], Kesavamoorthy Harini [us], Basoglu Christopher Hakan [us], Vikram Ayush [us], Shah Amy Parag [us], Kelkar Rakesh [us])

Abstract: [0000] A speculative decoding technique generates tokens of a response using a verifying model and drafting model. In operation, the verifying model receives a set of draft tokens produced by the drafting model. The verifying model then verifies whether the draft tokens are correct by comparing probability information generated by the verifying model with probability information generated by the drafting model. The verifying model then determines whether any draft token that is rejected by the v[...]


Our summary: Speculative decoding generates response tokens using a verifying and drafting model. The verifying model checks draft tokens against probability information from both models. This iterative process expands accepted tokens, reducing resource use and latency.

speculative decoding, token matching, verifying model, drafting model

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

Sujets abordés : Petits modèles linguistiques, traitement du langage naturel, systèmes basés sur des transformateurs, efficacité des paramètres, distillation des connaissances, compression des modèles, élagage structuré, élagage non structuré, quantification des poids, INT4, INT8, méthodes de réglage fin, déploiement sur l'appareil, latence d'inférence, consommation d'énergie, applications sensibles à la confidentialité, opérations à faible bande passante, contextes opérationnels hors ligne, IEEE 80211, ISO/IEC 30170, ISO/IEC 27001, ISO/IEC 25010, et NIST SP 800-53.

Glossaire des termes utilisés

Natural Language Processing (NLP): Domaine de l'intelligence artificielle axé sur l'interaction entre les ordinateurs et le langage humain, permettant aux machines de comprendre, d'interpréter et de générer du texte ou de la parole en langage naturel. Il englobe des tâches telles que la traduction, l'analyse des sentiments et la reconnaissance vocale.

Small Language Models (SLM): Les réseaux neuronaux compacts, conçus pour les tâches de traitement du langage naturel, se caractérisent généralement par un nombre réduit de paramètres et des exigences de calcul moindres par rapport aux modèles plus grands, tout en étant capables de générer un texte cohérent et de comprendre le contexte dans des limites limitées.

Contexte historique

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10e année anniversaire

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