The knowledge of tests conducted on a problematic vehicle is essential for enhancing the efficiency of mechanics. Therefore, identifying the tests performed in each repair case is of utmost importance. This thesis explores techniques for extracting data from unstructured repair reports to identify component tests. The main emphasis is on developing a supervised multi-class classifier to categorize data and extract sentences that describe repair diagnoses and actions. It has been shown that incorporating a category-aware contrastive learning objective can improve the repair report classifier’s performance. The proposed approach involves training a sentence representation model based on a pre-trained model using a category-aware contrastive learning objective. Subsequently, the sentence representation model is further trained on the classification task using a loss function that combines the cross-entropy and supervised contrastive learning losses. By applying this method, the macro F1-score on the test set is increased from 90.45 to 90.73. The attempt to enhance the performance of the repair report classifier using a noisy data classifier proves unsuccessful. The noisy data classifier is trained using a prompt-based fine-tuning method, incorporating open-ended questions and two examples in the prompt. This approach achieves an F1-score of 91.09 and the resulting repair report classification datasets are found easier to classify. However, they do not contribute to an improvement in the repair report classifier’s performance. Ultimately, the repair report classifier is utilized to aid in creating the input necessary for identifying component tests. An information retrieval method is used to conduct the test identification. The incorporation of this classifier and the existing labels when creating queries leads to an improvement in the mean average precision at the top 3, 5, and 10 positions by 0.62, 0.81, and 0.35, respectively, although with a slight decrease of 0.14 at the top 1 position.

As advancements in chatbots and Large Language Models (LLMs) such as GPT-3.5 and GPT-4 continue, their applications in diverse fields, including psychology, expand. This study investigates the effectiveness of LLMs optimized through prompt engineering, aiming to enhance their performance in psychological applications. To this end, two distinct versions of a GPT-3.5-based chatbot were developed: a version similar to the base model, and a version equipped with a more extensive system prompt detailing expected behavior. A panel of professional psychologists evaluated these models based on a predetermined set of questions, providing insight into their potential future use as psychological tools. Our results indicate that an overly prescriptive system prompt can unintentionally limit the versatility of the chatbot, making a careful balance in instruction specificity essential. Furthermore, while our study suggests that current LLMs such as GPT-3.5 are not capable of fully replacing human psychologists, they can provide valuable assistance in tasks such as basic question answering, consolation and validation, and triage. These findings provide a foundation for future research into the effective integration of LLMs in psychology and contribute valuable insights into the promising field of AI-assisted psychological services. / I takt med att framstegen inom chatbots och stora språkmodeller (LLMs) som GPT-3.5 och GPT-4 fortsätter utvidgas deras potentiella tillämpningar inom olika områden, inklusive psykologi. Denna studie undersöker effektiviteten av LLMs optimerade genom prompt engineering, med målet att förbättra deras prestanda inom psykologiska tillämpningar. I detta syfte utvecklades två distinkta versioner av en chatbot baserad på GPT-3.5: en version som liknar bas-modellen, och en version utrustad med en mer omfattande systemprompt som detaljerar förväntat beteende. En panel av professionella psykologer utvärderade dessa modeller baserat på en förbestämd uppsättning frågor, vilket ger inblick i deras potentiella framtida användning som psykologiska verktyg. Våra resultat tyder på att en överdrivet beskrivande systemprompt kan ofrivilligt begränsa chatbotens mångsidighet, vilket kräver en noggrann balans i specificiteten av prompten. Vidare antyder vår studie att nuvarande LLMs som GPT-3.5 inte kan ersätta mänskliga psykologer helt och hållet, men att de kan ge värdefull hjälp i uppgifter som grundläggande frågebesvaring, tröst och bekräftelse, samt triage. Dessa resultat ger en grund för framtida forskning om effektiv integration av LLMs inom psykologi och bidrar med värdefulla insikter till det lovande fältet av AI-assisterade psykologtjänster.

This graduation report explores the integration of Artificial Intelligence (AI) tools, specifically OpenAI's Generative Pre-trained Transformer (GPT) technology, into web development processes using WordPress (WP) for developing a AI-driven chat service. The focus of the project is on ImagineX AB, a private company that offers the educational service ChatGPT Utbildning aimed at teaching professionals to effectively utilize ChatGPT. The project is motivated by the rapid growth and adoption of AI tools such as ChatGPT, underpinned by the observed increase in user base and its integration into significant platforms, like Microsoft's Bing and Office packages. Despite its promising potential, the application of such AI tools in web development remains underexplored and untested in several aspects. The graduation report presents the implementation of a GPT model-driven chat service on the ChatGPT Utbildning WP website, enabling visitors to interact with the famous AI tool directly. This feature serves a dual purpose – enhancing user engagement and providing an instant demonstration of the utility of ChatGPT. The agile project management methodology in general is divided into four phases: preliminary work, design solutions, develop solution, and delivery – design and development phases are iterative. In this project, there is two design iterations and three development iterations called “cycles”. The project plan is fulfilled with no deviation. Tests and continuous improvements are done throughout the development, with specific and planned in each phase and cycle. The result is two optimized chat bots in respective well-designed chat boxes with full chat functionality driven by OpenAI API and GPT-3.5/GPT-4 models – user tested and then published on ChatGPT Utbildning website. Additionally, insights in agile management solutions in relation to AI tools have been produced. The detailed construction and in-depth discussion contribute to the wide understanding of AI implementation in web development, providing practical insights into the application of ChatGPT in a real-world setting by agile project management. Furthermore, it underscores the transformative potential of AI tools in shaping web solutions and web development, and propelling innovation in the field. The report delves into discussion of technology, ethics, society, and implications on future web development. / Rapporten ämnar redogöra integreringen av artificiell intelligens (AI) instrument, särskilt OpenAI's Generative Pre-trained Transformer (GPT) teknologi, inom ramen för webbutvecklingsprocesser, inklusive agil projektledning, med användning av WordPress (WP), i syfte att utveckla en AIdrivande chatttjänst. Fokus för projektet är på företaget ImagineX AB, en privat aktör som erbjuder en utbildningstjänst benämnd ChatGPT Utbildning med mål att undervisa yrkesverksamma i effektivt bruk av ChatGPT. Motivationen för projektet härstammar från den snabbt växande tillväxten och adoptionen av AI-instrument som ChatGPT, vilket stärks av den observerade tillväxten av användarbasen och dess integrering i betydande plattformar, såsom Microsofts Bing och Office-paket. Trots den lovande potential som dessa AIinstrument innehar, finns det fortfarande delar inom webbutveckling där användningen av sådana verktyg förblir ouppklarade och otillräckligt utforskade. Rapporten visar implementeringen av en GPT-modell-drivande chattjänst på ChatGPT Utbildning WP-webbplatsen, vilket möjliggör direkt interaktion för besökare med det framstående AI-instrumentet. Denna funktion har ett tvåfaldigt ändamål - att förhöja användarengagemang och att ge en omedelbar demonstration av ChatGPT:s användbarhet. Den använda smidiga projektledningsmetodiken är typiskt uppdelad i fyra faser: preliminärt arbete, designlösningar, utvecklingslösningar samt leverans - designoch utvecklingsfaser är iterativa vilket omfattar två designiterationer och tre utvecklingsiterationer refererade till som "cykler". Projektplanen har följts utan avvikelser. Testning och kontinuerliga förbättringar har genomförts under hela utvecklingsprocessen, med specifika och planerade insatser i varje fas och cykel. Resultatet manifesteras i två optimerade chattrobotar inom respektive välutformade chattfönster, med fullständig chattfunktionalitet som drivs av OpenAI API samt GPT-3.5/GPT-4 modellerna - vilka har användartestats och därefter publicerats på ChatGPT Utbildning webbplatsen. Ytterligare insikter rörande agil projektledning i relation till AI-frågor erhålls också. Den detaljerade konstruktionen och den djupgående diskussionen bidrar till en omfattande förståelse för AI-implementering inom webbutveckling och ger praktiska insikter om tillämpningen av ChatGPT i en realistisk inställning med smidig projektledning. Vidare framhäver det den transformerande potentialen hos AI-instrument för att utforma webblösningar och webbutveckling, vilket främjar innovation inom området. Rapporten avslutas med diskussioner kring teknik, etik, samhälle och implikationer för framtida webbutveckling.

From the minute a student walks into her first day of kindergarten, she is learning to read and write. Reading and writing are reciprocal in nature, using the same composing processes (Roe, Smith, & Kolodziej, 2019). Interchangeable thinking skills are essential for both reading and writing, such as analyzing, identifying, inferencing, evaluating, and comparing (Roe, Smith, & Koldziej, 2019). Published research over time suggested that instruction focused on teaching students the craft and mechanics of writing significantly contributed to the overall improvement across the spectrum of literacy development (Cutler, 2015;Raphael, 2019; Wright, 2016). However, studies also suggested that teachers of all grade levels tend to vary in their approach to teaching writing (Newmark, B., Speck, D., Amesbury, E., Lough, C., Belgutay, J., Lowe, J., … Hepburn, H, 2018). This study was focused on understanding how two elementary level teachers interpreted writing curriculum and carried out instruction in their respective classrooms. Qualitative methodological procedures were employed through interviewing both educators and observing their writing instruction. The collected data was analyzed through inductive thematic analysis and findings included: 1) both teachers believed that writing instruction matters; 2) both teachers followed the curriculum as they learned in teacher professional development; 3) writing instruction varied according to primary versus elementary contexts.

When reading a text, it is common to get stuck on unfamiliar words that are difficult to understand in the local context. In these cases, we use dictionaries or similar online resources to find the general meaning of the word. However, maintaining a handwritten dictionary is highly resource demanding as the language is constantly developing, and using generative language models for producing definitions could therefore be a more efficient option. To explore this possibility, this thesis performs an online survey to examine if GPT could be useful for defining words. It also investigates how well the Swedish language model GPT-SW3 (3.5 b) define words compared to the model text-davinci-003, and how prompts should be formatted when defining words with these models. The results indicate that text-davinci-003 generates high quality definitions, and according to students t-test, the definitions received significantly higher ratings from participants than definitions taken from Svensk ordbok (SO). Furthermore, the results showed that GPT-SW3 (3.5 b) received the lowest ratings, indicating that it takes more investment to keep up with the big models developed by OpenAI. Regarding prompt formatting, the most appropriate prompt format for defining words is highly dependent on the model, and the results showed that text- davinci-003 performed well using zero-shot, while GPT-SW3 (3.5 b) required a few shot setting. Considering both the high quality of the definitions generated by text-davinci-003, and the practical advantages with generating definitions automatically, GPT could be a useful method for defining words.

Question generation (QG), which automatically generates good-quality questions from a piece of text, is capable of lowering the cost of the manual composition of questions. Recently Question generation has attracted increasing interest for its ability to supply a large number of questions for developing conversation systems and educational applications, as well as corpus development for natural language processing (NLP) research tasks, such as question answering and reading comprehension. Previous neural-based QG approaches have achieved remarkable performance. In contrast, these approaches require a large amount of data to train neural models properly, limiting the application of question generation in low-resource scenarios, e.g. with a few hundred training examples. This thesis aims to address the problem of the low-resource scenario by investigating a recently emerged paradigm of NLP modelling, prompt-based learning. Prompt-based learning, which makes predictions based on the knowledge of the pre-trained language model and some simple textual task descriptions, has shown great effectiveness in various NLP tasks in few-shot and zero-shot settings, in which a few or non-examples are needed to train a model. In this project, we have introduced a prompt-based question generation approach by constructing question generation task instructions that are understandable by a pre-trained sequence-to-sequence language model. Our experiment results show that our approach outperforms previous state-of-the-art question generation models with a vast margin of 36.8%, 204.8%, 455.9%, 1083.3%, 57.9% for metrics BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE-L respectively in the few-shot learning settings. We also conducted a quality analysis of the generated questions and found that our approach can generate questions with correct grammar and relevant topical information when training with as few as 1,000 training examples.

This thesis explores the application of prompt engineering combined with human-AI interaction (HAII) to make text-to-image (TTI) models more accessible and intuitive for non-expert users. The thesis research focuses on developing an application with an intuitive interface that enables users to generate images without extensive knowledge of prompt engineering. A pre-post study was conducted to evaluate the application, demonstrating significant improvements in user satisfaction and ease of use. The findings suggest that such tailored interfaces can make AI technologies more accessible, empowering users to engage creatively with minimal technical barriers. This study contributes to the fields of Media technology and AI by showcasing how simplifying prompt engineering can enhance the accessibility of generative AI tools. / Detta examensarbete utforskar tillämpningen av prompt engineering i kombination med human-AI interaction för att göra text-till-bild modeller mer tillgängliga och intuitiva för icke-experter. Forskningen för examensarbetet fokuseras på att utveckla en applikation med ett intuitivt gränssnitt som gör det möjligt för användare att generera bilder utan omfattande kunskaper om prompt engineering. En före-efter-studie genomfördes för att utvärdera applikationen, vilket visade på en tydlig ökning i användarnöjdhet och användarvänlighet. Utfallet från studien tyder på att skräddarsydda gränssnitt kan göra AI-tekniken mer tillgänglig, och göra det möjligt för användare att nyttja det kreativa skapandet med minimerade tekniska hinder. Den här studien bidrar till områdena avmedieteknik och AI genom att demonstrera hur prompt engineering kan förenklas vilket kan förbättra tillgängligheten av AI-verktyg.

Making information stored in databases more accessible to users inexperienced in structured query language (SQL) by converting natural language to SQL queries has long been a prominent research area in both the database and natural language processing (NLP) communities. There have been numerous approaches proposed for this task, such as encoder-decoder frameworks, semantic grammars, and more recently with the use of large language models (LLMs). When training LLMs to successfully generate SQL queries from natural language questions there are three notable methods used, pretraining, transfer learning and in-context learning (ICL). ICL is particularly advantageous in scenarios where the hardware at hand is limited, time is of concern and large amounts of task specific labled data is nonexistent. This study seeks to evaluate two strategies in ICL, namely zero-shot and few-shot scenarios using the Mistral-7B-Instruct LLM. Evaluation of the few-shot scenarios was conducted using two techniques, random selection and Jaccard Similarity. The zero-shot scenarios served as a baseline for the few-shot scenarios to overcome, which ended as anticipated, with the few-shot scenarios using Jaccard similarity outperforming the other two methods, followed by few-shot scenarios using random selection coming in at second best, and the zero-shot scenarios performing the worst. Evaluation results acquired based on execution accuracy and exact matching accuracy confirm that leveraging similarity in demonstrating examples when prompting the LLM will enhance the models knowledge about the database schema and table names which is used during the inference phase leadning to more accurately generated SQL queries than leveraging diversity in demonstrating examples.

Ion beam therapy is an innovative radiotherapy technique using mainly carbon ion and proton irradiations. Its aim is to improve the current treatment modalities. Because of the sharpness of the dose distributions, a control of the dose if possible in real time is highly desirable. A possibility is to detect the prompt gamma rays emitted subsequently to the nuclear fragmentations occurring during the treatment of the patient. In a first time two different Compton cameras (double and single scattering) have been optimised by means of Monte Carlo simulations. The response of the camera to a photon point source with a realistic energy spectrum was studied. Then, the response of the camera to the irradiation of a water phantom by a proton beam was simulated. It was first compared with measurement performed with small-size detectors. Then, using the previous measurements, we evaluated the counting rates expected in clinical conditions. In the current set-up of the camera, these counting rates are pretty high. Pile up and random coincidences will be problematic. Finally we demonstrate that the detection system is capable to detect a longitudinal shift in the Bragg peak of +or- 5 mm, even with the current reconstruction algorithm.

Au cours du traitement d'une tumeur avec des ions légers, la position du pic de Bragg doit être connue avec précision. Une fraction importante des ions incidents subissent des collisions nucléaires avec les noyaux cibles conduisant à l'émission de particules promptes qui peuvent être porteuses d'informations sur le parcours des ions. Ce travail, qui se concentre sur les gamma prompts, montre que le rendement en profondeur de ces émissions est fortement corrélé au parcours des ions et que les taux de comptage mesurés permettent d'envisager un système d'imagerie réaliste, fonctionnant en temps réel. Dans ce but, nous avons réalisé des expériences au GANIL et au GSI avec un détecteur collimaté placé perpendiculairement à l'axe du faisceau et la technique du temps de vol a été utilisée pour réduire le bruit de fond induit par les neutrons et les particules chargées. Des simulations Geant4 ont été réalisées pour concevoir le dispositif expérimental et interpréter les données. Un accord qualitatif entre les simulations et l'expérience est observé pour la quantité d'énergie déposée dans le détecteur et pour la forme du spectre de temps de vol. Cependant, des divergences apparaissent pour le rendement de gamma prompts et la distribution en profondeur des gamma détectés. Ces divergences sont discutées, principalement en termes de modèles de physique nucléaire qui doivent être améliorés. Après avoir sélectionné les modèles physiques offrant les simulations les plus en accord avec les mesures, des études concernant les lieux d'émissions des gamma prompts et l'influence de la diffusion dans la cible ont été réalisés afin de déterminer l'impact sur la corrélation avec le parcours des ions / During the treatment of a tumor with light ions, the Bragg peak location must be accurately known. A significant fraction of the incident ions undergo nuclear collisions with the target nuclei leading to the prompt emission of particles which may carry information on the ion path. This work, which focuses on prompt gamma, shows that the depth profile of these emissions is highly correlated to the ions path and the measured counting rates allow to consider a realistic imaging system, operating in real time. For that purpose, we performed experiments at GANIL and at GSI with a collimated detector placed perpendicular to the beam axis and the time of flight technique was used in order to reduce the noise induced by neutrons and charged particles. Geant4 simulations were performed for the experimental design and data interpretation. A qualitative agreement between simulations and experiment is observed for the amount of energy deposited in the detector and the shape of the time of flight spectrum. However, discrepancies appear for the prompt gamma yield and the depth distribution of gamma detected. These discrepancies are discussed, mainly in terms of nuclear physics models that must be improved. After selecting the physical models which lead to the best agreement between simulations and measurements, studies on the location of prompt gamma emission and on the influence of diffusion in the target were performed to determine the impact on the correlation with the ion path