To label words of interest into a predefined set of named entities have traditionally required a large amount of labeled in-domain data. Recently, the availability of pre-trained transformer-based language models have enabled multiple natural language processing problems to utilize transfer learning techniques to construct machine learning models with less task-specific labeled data. In this thesis, the impact of data augmentation when training a pre-trained transformer-based model to adapt to a named entity recognition task with few labeled sentences is explored. The experimental results indicate that data augmentation increases performance of the trained models, however the data augmentation is shown to have less impact when more labeled data is available. In conclusion, data augmentation has been shown to improve performance of pre-trained named entity recognition models when few labeled sentences are available for training. / Att kategorisera ord som tillhör någon av en mängd förangivna entiteter har traditionellt krävt stora mängder förkategoriserad områdesspecifik data. På senare år har det tillgängliggjorts förtränade språkmodeller som möjliggjort för språkprocesseringsproblem att lösas med en mindre mängd områdesspecifik kategoriserad data. I den här uppsatsen utforskas datautöknings påverkan på en maskininlärningsmodell för identifiering av namngivna entiteter. De experimentella resultaten indikerar att datautökning förbättrar modellerna, men att inverkan blir mindre när mer kategoriserad data är tillgänglig. Sammanfattningsvis så kan datautökning förbättra modeller för identifiering av namngivna entiteter när få förkategoriserade meningar finns tillgängliga för träning.

This thesis investigates the possibility of automating the classification of post-feedback mathematical reasoning styles, Creative Mathematical Reasoning (CMR) and Algorithmic Reasoning (AR), using prompt-based classification with a Large Language Model (LLM). The study, conducted in collaboration with the Department of Science and Mathematics Education of Umeå University, aims to enhance the efficiency of pedagogical research by reducing the manual labor involved in classifying student responses. The thesis utilizes a dataset of 40 expert-labeled student mathematical solutions, incorporating feedback interactions to assess shifts in reasoning post-feedback. Various prompting methods, including definitions-only and examples-inclusive prompts, were systematically tested to determine their effectiveness in classifying reasoning styles. The classification performance was measured using accuracy, F1-score, and Cohen’s kappa. Results indicate that definitionbased prompts performed robustly, achieving moderate to strong inter-rater agreement. The study also explored the impact of output formats and found that allowing the LLM to classify uncertain cases as indeterminate could potentially automate about 25% of the classification tasks without compromising performance. This thesis underscores the potential of LLMs in automating complex cognitive task classifications in educational research, suggesting further exploration into optimal prompting strategies and reliability enhancements for practical applications. / Denna uppsats undersöker möjligheten att automatisera klassificeringen av matematiska resonemangstyper efter feedback, Kreativt Matematiskt Resonemang (CMR) och Algoritmiskt Resonemang (AR), med hjälp av promptbaserad klassificering med en stor språkmodell (LLM). Studien, som genomfördes i samarbete med Institutionen för naturvetenskapernas och matematikens didaktik vid Umeå universitet, syftar till att öka effektiviteten i pedagogisk forskning genom att minska det manuella arbetet som krävs för att klassificera studenters matematiska resonemang. Uppsatsen använder ett dataset med 40 matematiska lösningar från studenter, klassificerade av experter. Dessa lösningar inkluderar feedback-interaktioner för att bedöma förändringar i resonemang efter feedback. Olika promptmetoder, innehållandes enbart definitioner och exempel-inkluderande promptar, testades systematiskt för att avgöra deras effektivitet vid klassificering av resonemangsstilar. Klassificeringsprestandan mättes med hjälp av accuracy, F1-score och Cohen’s kappa. Resultaten visar att promptar baserade på definitioner hade en robust prestanda och uppnådde måttlig till stark överensstämmelse mellan bedömare. Studien undersökte också påverkan av utdataformat och fann att genom att tillåta LLM att klassificera osäkra fall som obestämdbarkunde cirka 25% av klassificeringsuppgifterna automatiseras utan att kompromissa med prestandan. Denna avhandling framhäver potentialen hos LLMs att automatisera komplexa kognitiva uppgiftsklassificeringar inom utbildningsforskning och föreslår vidare studier av optimala promptstrategier och tillförlitlighetsförbättringar för praktiska tillämpningar.

SOTA language models have demonstrated remarkable capabilities in tackling NLP tasks they have not been explicitly trained on – given a few demonstrations of the task (few-shot learning), or even none at all (zero-shot learning). The purpose of this Master’s thesis has been to investigate practical aspects and potential applications of zero/few-shot learning in the context of text classification. This includes topics such as combined usage with active learning, automated data labeling, and interpretability. Two different methods for zero/few-shot learning have been investigated, and the results indicate that:  • Active learning can be used to marginally improve few-shot performance, but it seems to be mostly beneficial in settings with very few samples (e.g. less than 10). • Zero-shot learning can be used produce reasonable candidate labels for classes in a dataset, given knowledge of the classification task at hand.  • It is difficult to trust the predictions of zero-shot text classification without access to a validation dataset, but IML methods such as saliency maps could find usage in debugging zero-shot models. / Ledande språkmodeller har uppvisat anmärkningsvärda förmågor i att lösa NLP-problem de inte blivit explicit tränade på – givet några exempel av problemet (few-shot learning), eller till och med inga alls (zero-shot learning). Syftet med det här examensarbetet har varit att undersöka praktiska aspekter och potentiella tillämpningar av zero/few-shot learning inom kontext av textklassificering. Detta inkluderar kombinerad användning med aktiv inlärning, automatiserad datamärkning, och tolkningsbarhet. Två olika metoder för zero/few-shot learning har undersökts, och resultaten indikerar att: • Aktiv inlärning kan användas för att marginellt förbättra textklassificering med few-shot learning, men detta verkar vara mest fördelaktigt i situationer med väldigt få datapunkter (t.ex. mindre än 10). • Zero-shot learning kan användas för att hitta lämpliga etiketter för klasser i ett dataset, givet kunskap om klassifikationsuppgiften av intresse. • Det är svårt att lita på robustheten i textklassificering med zero-shot learning utan tillgång till valideringsdata, men metoder inom tolkningsbar maskininlärning såsom saliency maps skulle kunna användas för att felsöka zero-shot modeller.

Dans ce mémoire, nous étudions la généralisation des réseaux de neurones dans le contexte du méta-apprentissage, en analysant divers propriétés des surface leurs fonctions objectifs. La recherche en apprentissage automatique portant sur les surfaces de fonctions objectifs des réseaux de neurones ayant aidé à comprendre leur généralisation en apprentissage supervisé standard, nous proposons l'étude de telles surfaces dans le but d'approfondir nos connaissances sur la généralisation en méta-apprentissage. Nous introduisons d'abord la littérature sur les fonctions objectifs des réseaux de neurones à la Section \ref{sec:intro:objective_landscapes}, puis celle portant sur le méta-apprentissage à la Section \ref{sec:intro:meta-learning}, pour enfin terminer notre introduction avec le méta-apprentissage par descente de gradient, très similaire à l'entraînement des réseaux de neurones par descente de gradient stochastique et pour une tâche unique. Nous présentons par la suite notre travail sur les fonctions objectifs en méta-apprentissage au Chapitre \ref{chap:prof_forcing}, lequel nous avons soumis à la conférence NeurIPS 2019 en tant qu'article scientifique. Au moment d'écrire ce mémoire, et au meilleur de notre connaissance, ce travail est le premier à étudier empiriquement les surfaces des fonctions objectifs en méta-apprentissage, particulièrement dans le contexte de l'apprentissage profond, et nous mettons notamment en lumière certaines propriétés de ces surfaces qui apparaissent liées à la généralisation des réseaux de neurones à de nouvelles tâches. Nous démontrons empiriquement qu'alors que progresse la phase de méta-entraînement, pour les solutions aux nouvelles tâches obtenues via quelques itérations de descente de gradient, la courbure de la fonction objective décroit monotoniquement, la valeur de la fonction objective diminue, tandis que la distance euclidienne avec la solution ``méta-entraînement" augmente. Cependant, nous observons que la courbure des minima continue de décroître même lorsque le sur-apprentissage devient apparent et que la généralisation commence à se dégrader, indiquant que la courbure des minima semble peu corrélée à la généralisation en méta-apprentissage par descente de gradient. De plus, nous montrons empiriquement que la généralisation aux nouvelles tâches semble plutôt liée à la cohérence de leurs trajectoires d'adaptation dans l'espace des paramètres, mesurée par la similarité cosinus moyenne entre les trajectoires. Nous montrons également que la cohérence des gradients ''meta-test", mesurée par le produit scalaire moyen entre les vecteurs de gradients spécifiques aux nouvelles tâches, évalué à solution meta-entraînement, est également corrélée à la généralisation. Nous basant sur ces observations, nous proposons un nouveau terme de régularisation pour l'algorithme de méta-apprentissage Model Agnostic Meta-Learning (MAML). / In this master's thesis, we study the generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes. Meta-learning, a challenging paradigm where models not only have to learn a task but beyond that, are trained for ``learning to learn" as they must adapt to new tasks and environments with very limited data about them. With research on the objective landscapes of neural networks in classical supervised having provided some answers regarding their ability to generalize for new data points, we propose similar analyses aimed at understanding generalization in meta-learning. We first introduce the literature on objective landscapes of neural networks in Section \ref{sec:intro:objective_landscapes}. We then introduce the literature of meta-learning in Section \ref{chap:prof_forcing}, concluding our introduction with the approach of gradient-based meta-learning, a meta-learning setup that bears strong similarities to the traditional supervised learning setup through stochastic gradient-based optimization. At the time of writing of this thesis, and to the best of our knowledge, this is the first work to empirically study the objective landscapes in gradient-based meta-learning, especially in the context of deep learning. We notably provide some insights on some properties of those landscapes that appear correlated to the generalization to new tasks. We experimentally demonstrate that as meta-training progresses, the meta-test solutions, obtained after adapting the meta-train solution of the model, to new tasks via few steps of gradient-based fine-tuning, become flatter, lower in loss, and further away from the meta-train solution. We also show that those meta-test solutions become flatter even as generalization starts to degrade, thus providing experimental evidence against the correlation between generalization and flat minima in the paradigm of gradient-based meta-leaning. Furthermore, we provide empirical evidence that generalization to new tasks is correlated with the coherence between their adaptation trajectories in parameter space, measured by the average cosine similarity between task-specific trajectory directions, starting from a same meta-train solution. We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization. Based on these observations, we propose a novel regularizer for the Model Agnostic Meta-Learning (MAML) algorithm and provide experimental evidence for its effectiveness.

The rapid growth of textual data in the digital age presents unique challenges in domain-specific text classification, particularly the scarcity of labeled data for many applications, due to expensive cost of manual labeling work. In this thesis, we explore the applicability of prompt-learning method, which is well-known for being suitable in few-shot scenarios and much less data-consuming, as an emerging alternative to traditional fine-tuning methods, for domain-specific text classification in the context of customer-agent interactions in the retail sector. Specifically, we implemented the entire prompt-learning pipeline for the classification task, and, our investigation encompasses various strategies of prompt-learning, including fixed-prompt language model tuning strategy and tuning-free prompting strategy, along with an examination of language model selection, few-shot sampling strategy, prompt template design, and verbalizer design. In this manner, we assessed the overall performance of the prompt-learning method in the classification task. Through a systematic evaluation, we demonstrate that with the fixed-prompt language model tuning strategy, based on relatively smaller language models (e.g. T5-base with around 220M parameters), prompt-learning can achieve competitive performance (close to 75% accuracy) even with limited labeled data (up to merely 15% of full data). And besides, with the tuning-free prompting strategy, based on a regular-size language model (e.g. FLAN-T5-large with around 770M parameters), the performance can be up to around 30% accuracy with detailed prompt templates and zero-shot setting (no extra training data involved). These results can offer valuable insights for researchers and practitioners working with domain-specific textual data, prompt-learning and few-shot / zero-shot learning. The findings of this thesis highlight the potential of prompt-learning as a practical solution for classification problems across diverse domains and set the stage for future research in this area.

Text-to-SQL, which involves translating natural language into Structured Query Language (SQL), is crucial for enabling broad access to structured databases without expert knowledge. However, designing models for such tasks is challenging due to numerous factors, including the presence of ’noise,’ such as ambiguous questions and syntactical errors. This thesis provides an in-depth analysis of the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on models. While BIRD-Bench was created to model dirty and noisy database values, it was not created to contain noise and errors in the questions and gold queries. We found after a manual evaluation that noise in questions and gold queries are highly prevalent in the financial domain of the dataset, and a further analysis of the other domains indicate the presence of noise in other parts as well. The presence of incorrect gold SQL queries, which then generate incorrect gold answers, has a significant impact on the benchmark’s reliability. Surprisingly, when evaluating models on corrected SQL queries, zero-shot baselines surpassed the performance of state-of-the-art prompting methods. The thesis then introduces the concept of classifying noise in natural language questions, aiming to prevent the entry of noisy questions into text-to-SQL models and to annotate noise in existing datasets. Experiments using GPT-3.5 and GPT-4 on a manually annotated dataset demonstrated the viability of this approach, with classifiers achieving up to 0.81 recall and 80% accuracy. Additionally, the thesis explored the use of LLMs for automatically correcting faulty SQL queries. This showed a 100% success rate for specific query corrections, highlighting the potential for LLMs in improving dataset quality. We conclude that informative noise labels and reliable benchmarks are crucial to developing new Text-to-SQL methods that can handle varying types of noise.

Ces dernières années, le domaine de l’apprentissage profond a connu des progrès énormes dans des applications allant de la génération d’images, détection d’objets, modélisation du langage à la réponse aux questions visuelles. Les approches classiques telles que l’apprentissage supervisé nécessitent de grandes quantités de données étiquetées et spécifiques à la tâches. Cependant, celles-ci sont parfois coûteuses, peu pratiques, ou trop longues à collecter. La modélisation efficace en données, qui comprend des techniques comme l’apprentissage few-shot (à partir de peu d’exemples) et l’apprentissage self-supervised (auto-supervisé), tentent de remédier au manque de données spécifiques à la tâche en exploitant de grandes quantités de données plus “générales”. Les progrès de l’apprentissage profond, et en particulier de l’apprentissage few-shot, s’appuient sur les benchmarks (suites d’évaluation), les métriques d’évaluation et les jeux de données, car ceux-ci sont utilisés pour tester et départager différentes méthodes sur des tâches précises, et identifier l’état de l’art. Cependant, du fait qu’il s’agit de versions idéalisées de la tâche à résoudre, les benchmarks sont rarement équivalents à la tâche originelle, et peuvent avoir plusieurs limitations qui entravent leur rôle de sélection des directions de recherche les plus prometteuses. De plus, la définition de métriques d’évaluation pertinentes peut être difficile, en particulier dans le cas de sorties structurées et en haute dimension, telles que des images, de l’audio, de la parole ou encore du texte. Cette thèse discute des limites et des perspectives des benchmarks existants, des fonctions de coût (training losses) et des métriques d’évaluation (evaluation metrics), en mettant l’accent sur la modélisation générative - les Réseaux Antagonistes Génératifs (GANs) en particulier - et la modélisation efficace des données, qui comprend l’apprentissage few-shot et self-supervised. La première contribution est une discussion de la tâche de modélisation générative, suivie d’une exploration des propriétés théoriques et empiriques des fonctions de coût des GANs. La deuxième contribution est une discussion sur la limitation des few-shot classification benchmarks, certains ne nécessitant pas de généralisation à de nouvelles sémantiques de classe pour être résolus, et la proposition d’une méthode de base pour les résoudre sans étiquettes en phase de testing. La troisième contribution est une revue sur les méthodes few-shot et self-supervised de détection d’objets , qui souligne les limites et directions de recherche prometteuses. Enfin, la quatrième contribution est une méthode efficace en données pour la description de vidéo qui exploite des jeux de données texte et vidéo non supervisés. / In recent years, the field of deep learning has seen tremendous progress for applications ranging from image generation, object detection, language modeling, to visual question answering. Classic approaches such as supervised learning require large amounts of task-specific and labeled data, which may be too expensive, time-consuming, or impractical to collect. Data-efficient methods, such as few-shot and self-supervised learning, attempt to deal with the limited availability of task-specific data by leveraging large amounts of general data. Progress in deep learning, and in particular, few-shot learning, is largely driven by the relevant benchmarks, evaluation metrics, and datasets. They are used to test and compare different methods on a given task, and determine the state-of-the-art. However, due to being idealized versions of the task to solve, benchmarks are rarely equivalent to the original task, and can have several limitations which hinder their role of identifying the most promising research directions. Moreover, defining meaningful evaluation metrics can be challenging, especially in the case of high-dimensional and structured outputs, such as images, audio, speech, or text. This thesis discusses the limitations and perspectives of existing benchmarks, training losses, and evaluation metrics, with a focus on generative modeling—Generative Adversarial Networks (GANs) in particular—and data-efficient modeling, which includes few-shot and self-supervised learning. The first contribution is a discussion of the generative modeling task, followed by an exploration of theoretical and empirical properties of the GAN loss. The second contribution is a discussion of a limitation of few-shot classification benchmarks, which is that they may not require class semantic generalization to be solved, and the proposal of a baseline method for solving them without test-time labels. The third contribution is a survey of few-shot and self-supervised object detection, which points out the limitations and promising future research for the field. Finally, the fourth contribution is a data-efficient method for video captioning, which leverages unsupervised text and video datasets, and explores several multimodal pretraining strategies.

Tesis por compendio / [ES] En la última década, el aprendizaje profundo (DL) se ha convertido en la principal herramienta para las tareas de visión por ordenador (CV). Bajo el paradigma de aprendizaje supervisado, y gracias a la recopilación de grandes conjuntos de datos, el DL ha alcanzado resultados impresionantes utilizando redes neuronales convolucionales (CNNs). Sin embargo, el rendimiento de las CNNs disminuye cuando no se dispone de suficientes datos, lo cual dificulta su uso en aplicaciones de CV en las que sólo se dispone de unas pocas muestras de entrenamiento, o cuando el etiquetado de imágenes es una tarea costosa. Estos escenarios motivan la investigación de estrategias de aprendizaje menos supervisadas. En esta tesis, hemos explorado diferentes paradigmas de aprendizaje menos supervisados. Concretamente, proponemos novedosas estrategias de aprendizaje autosupervisado en la clasificación débilmente supervisada de imágenes histológicas gigapixel. Por otro lado, estudiamos el uso del aprendizaje por contraste en escenarios de aprendizaje de pocos disparos para la vigilancia automática de cruces de ferrocarril. Por último, se estudia la localización de lesiones cerebrales en el contexto de la segmentación no supervisada de anomalías. Asimismo, prestamos especial atención a la incorporación de conocimiento previo durante el entrenamiento que pueda mejorar los resultados en escenarios menos supervisados. En particular, introducimos proporciones de clase en el aprendizaje débilmente supervisado en forma de restricciones de desigualdad. Además, se incorpora la homogeneización de la atención para la localización de anomalías mediante términos de regularización de tamaño y entropía. A lo largo de esta tesis se presentan diferentes métodos menos supervisados de DL para CV, con aportaciones sustanciales que promueven el uso de DL en escenarios con datos limitados. Los resultados obtenidos son prometedores y proporcionan a los investigadores nuevas herramientas que podrían evitar la anotación de cantidades masivas de datos de forma totalmente supervisada. / [CA] En l'última dècada, l'aprenentatge profund (DL) s'ha convertit en la principal eina per a les tasques de visió per ordinador (CV). Sota el paradigma d'aprenentatge supervisat, i gràcies a la recopilació de grans conjunts de dades, el DL ha aconseguit resultats impressionants utilitzant xarxes neuronals convolucionals (CNNs). No obstant això, el rendiment de les CNNs disminueix quan no es disposa de suficients dades, la qual cosa dificulta el seu ús en aplicacions de CV en les quals només es disposa d'unes poques mostres d'entrenament, o quan l'etiquetatge d'imatges és una tasca costosa. Aquests escenaris motiven la investigació d'estratègies d'aprenentatge menys supervisades. En aquesta tesi, hem explorat diferents paradigmes d'aprenentatge menys supervisats. Concretament, proposem noves estratègies d'aprenentatge autosupervisat en la classificació feblement supervisada d'imatges histològiques gigapixel. D'altra banda, estudiem l'ús de l'aprenentatge per contrast en escenaris d'aprenentatge de pocs trets per a la vigilància automàtica d'encreuaments de ferrocarril. Finalment, s'estudia la localització de lesions cerebrals en el context de la segmentació no supervisada d'anomalies. Així mateix, prestem especial atenció a la incorporació de coneixement previ durant l'entrenament que puga millorar els resultats en escenaris menys supervisats. En particular, introduïm proporcions de classe en l'aprenentatge feblement supervisat en forma de restriccions de desigualtat. A més, s'incorpora l'homogeneïtzació de l'atenció per a la localització d'anomalies mitjançant termes de regularització de grandària i entropia. Al llarg d'aquesta tesi es presenten diferents mètodes menys supervisats de DL per a CV, amb aportacions substancials que promouen l'ús de DL en escenaris amb dades limitades. Els resultats obtinguts són prometedors i proporcionen als investigadors noves eines que podrien evitar l'anotació de quantitats massives de dades de forma totalment supervisada. / [EN] In the last decade, deep learning (DL) has become the main tool for computer vision (CV) tasks. Under the standard supervised learnng paradigm, and thanks to the progressive collection of large datasets, DL has reached impressive results on different CV applications using convolutional neural networks (CNNs). Nevertheless, CNNs performance drops when sufficient data is unavailable, which creates challenging scenarios in CV applications where only few training samples are available, or when labeling images is a costly task, that require expert knowledge. Those scenarios motivate the research of not-so-supervised learning strategies to develop DL solutions on CV. In this thesis, we have explored different less-supervised learning paradigms on different applications. Concretely, we first propose novel self-supervised learning strategies on weakly supervised classification of gigapixel histology images. Then, we study the use of contrastive learning on few-shot learning scenarios for automatic railway crossing surveying. Finally, brain lesion segmentation is studied in the context of unsupervised anomaly segmentation, using only healthy samples during training. Along this thesis, we pay special attention to the incorporation of tasks-specific prior knowledge during model training, which may be easily obtained, but which can substantially improve the results in less-supervised scenarios. In particular, we introduce relative class proportions in weakly supervised learning in the form of inequality constraints. Also, attention homogenization in VAEs for anomaly localization is incorporated using size and entropy regularization terms, to make the CNN to focus on all patterns for normal samples. The different methods are compared, when possible, with their supervised counterparts. In short, different not-so-supervised DL methods for CV are presented along this thesis, with substantial contributions that promote the use of DL in data-limited scenarios. The obtained results are promising, and provide researchers with new tools that could avoid annotating massive amounts of data in a fully supervised manner. / The work of Julio Silva Rodríguez to carry out this research and to elaborate this dissertation has been supported by the Spanish Government under the FPI Grant PRE2018-083443. / Silva Rodríguez, JJ. (2022). Learning from limited labelled data: contributions to weak, few-shot, and unsupervised learning [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/190633 / Compendio

<p>Nuclear engineering hosts diverse domains including, but not limited to, power plant automation, human-machine interfacing, detection and identification of special nuclear materials, modeling of reactor kinetics and dynamics that most frequently are described by systems of differential equations (DEs), either ordinary (ODEs) or partial ones (PDEs). In this work we study multiple problems related to safety and Special Nuclear Material detection, and numerical solutions for partial differential equations using neural networks. More specifically, this work is divided in six chapters. Chapter 1 is the introduction, in Chapter</p> <p>2 we discuss the development of a gamma-ray radionuclide library for the characterization</p> <p>of gamma-spectra. In Chapter 3, we present a new approach, the ”Variance Counterbalancing”, for stochastic</p> <p>large-scale learning. In Chapter 4, we introduce a systematic approach for constructing proper trial solutions to partial differential equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions. Chapter 5 is about an alternative, less imposing development of neural-form trial solutions for PDEs, inside rectangular and non-rectangular convex boundaries. Chapter 6 presents an ensemble method that avoids the multicollinearity issue and provides</p> <p>enhanced generalization performance that could be suitable for handling ”few-shots”- problems frequently appearing in nuclear engineering.</p>

Les modélisateurs rencontrent souvent des défis ou des difficultés lorsqu’il s’agit de concevoir un modèle logiciel particulier. Dans cette thèse, nous avons exploré différentes voies et examiné différentes approches pour résoudre cette problématique. Nous proposons enfin une approche simple mais novatrice qui améliore la complétion des activités de modélisation de domaines. Cette approche exploite la puissance des modèles de langage de grande taille en utilisant l’apprentissage par seulement quelques exemples, éliminant ainsi la nécessité d’un apprentissage profond ou d’un ajustement fin (fine tuning) sur des ensembles de données rares dans ce domaine. L’un des points forts de notre approche est sa polyvalence, car elle peut s’intégrer fa cilement à de nombreuses activités de modélisation, fournissant un aide précieux et des recommendations aux modélisateurs. De plus, nous avons mené une étude utilisateur pour évaluer l’utilité de cette méthode et la valeur de l’assistance en modélisation; nous avons cherché à savoir si l’effort investi dans l’assistance en modélisation vaut la peine en recueillant les commentaires des concepteurs de modèles logiciels. / Modelers often encounter challenges or difficulties when it comes to designing a particular software model. Throughout this thesis, we have explored various paths and examined different approaches to address this issue. We finally propose a simple yet novel approach enhancing completion in domain modeling activities. This approach leverages the power of large language models by utilizing few-shot prompt learning, eliminating the need for extensive training or fine-tuning on scarce datasets in this field. One of the notable strengths of our approach lies in its versatility, as it can be seamlessly integrated into various modeling activities, providing valuable support and recommendations to software modelers. Additionally, we conducted a user study to evaluate the usefulness of this approach and determine the value of providing assistance in modeling; we aimed to determine if the effort invested in modeling assistance is worthwhile by gathering feedback from software modelers.