The loss of senior expertise in power line design poses a critical challenge to the sustainable energy transition. Current methods of knowledge transfer fail to prevent the loss of invaluable knowledge necessary for future junior power line designers. Additionally, the rise of informal deployment of generative language models may also threaten to bury hand-written knowledge documents before this knowledge can be extracted, structured, and preserved for future guidance. This thesis proposes a framework where large language models are integrated into knowledge transfer and decision-making guidance for an engineering enterprise. Using this framework, this thesis further explores how data-driven knowledge tools can assist junior design engineers by supporting information retrieval and directing to knowledge sources. The ability of a large language model to retrieve relevant knowledge from an engineering design document was validated by comparing the process of human designers manually completing a similar task. In this evaluation involving six participants and the large language model, responses to questions on mechanical dimensioning of stays for utility poles were ranked by experts. The results showed that the large language model responses were ranked similarly to the junior designers on average. Additionally, a small-scale demonstrative knowledge tool, insights from interviews, literature studies as well as the results from the validation study lead to the conclusion that large language models can assist power line designers via a knowledge tool. Beyond power line design, this thesis contributes to the understanding of how data-driven language models can assist knowledge retrieval and decision-making across other engineering design domains. This work utilizes a professional education document on the mechanical dimensioning of wooden power line poles including an analysis on the wind and weight span’s affect on the dimension of the pole, developed parallel to this work. The original design data from the document supported the tests conducted in this thesis. The professional education document on the mechanical dimensioning of wooden power line poles was developed in parallel to this thesis as a case study supporting the tests with original design data on power line design knowledge. The work also discusses risks and ethical aspects when implementing such a knowledge tool. Risks such as leakage of classified information are emphasized and need comprehensive systems and methods to be avoided. It is therefore highlighted how important it is to carry out the project with care and expertise to avoid damage to companies and society. Local language models or highly trusted AI system providers are recommended to ensure that no sensitive information is leaked to an unwanted third-party. With a high degree of caution and consideration of risks, an effective knowledge tool can contribute to increased efficiency, faster and more sustainable development of power line infrastructure, and thus an faster energy transition. / Förlusten av senior expertis inom kraftledningskonstruktion utgör en kritisk utmaning för den hållbara energiomställningen. Nuvarande metoder för kunskapsöverföring är otillräcklig för att förhindra förlusten av ovärderlig kunskap som är nödvändig för framtida juniora kraftledningsprojektörer. Dessutom kan den ökade informella användingen av generativa språkmodeller hota att begrava mänskligt skrivna kunskapsdokument. Detta arbete presenterar ett ramverk d¨ar storskaliga språkmodeller används för att underlätta kunskapsöverföring och tillhandahålla vägledning vid beslutsfattande inom ingenjörsföretag. Med hjälp av detta ramverk utforskar arbetet ytterligare hur datadrivna kunskapsverktyg kan hjälpa juniora kraftledningskonstrukt¨orer genom att stödja informationsinhämtning med hänvisning till kunskapskällorna. En storskalig språkmodells förmåga att hämta relevant kunskap från ett tekniskt designdokument validerades genom att jämföra processen för mänskliga designers som manuellt slutförde en liknande uppgift. I denna utv¨ardering, som involverade sex deltagare och den storskaliga spr˚akmodellen, rankades svaren på frågor om mekanisk dimensionering av stag för kraftledningsstolpar av experter. Resultaten visade att den storskaliga språkmodellens svar i genomsnitt rankades på liknade nivå som de juniora ingenjörerna. Tillsammans med  ett småskaligt demonstrativt kunskapsverktyg, insikter från intervjuer med kraftledningskonstruktörer, litteraturstudier samt resultat från valideringsstudien dras slutsatsen att storskaliga språkmodeller kan stödja kraftledningskonstruktörer via ett kunskapsverktyg. Utöver kraftledningskonstruktion bidrar detta arbete till förståelsen av hur datadrivna språkmodeller kan hjälpa till med kunskapsinhämtning och beslutsfattande  inom andra tekniska designområden. Arbetet använder ett professionellt utbildningsunderlag om mekanisk dimensionering av kraftledningsstolpar i träkonstruktion, inklusive en analys av vertikala- och horistontella linspannets påverkan på stolpens dimension, utvecklat parallellt med detta arbete. Orginaldesigndata från underlaget stödde de tester som genomfördes. Arbetet belyser även risker och etiska aspekter vid implementering av ett sådant kunskapsverktyg. Risker som läckage av sekretessbelagd information betonas, och omfattande system och metoder behövs för att undvika dem. Därför understryks hur viktigt det är att genomföra liknande projekt med noggrannhet, försiktighet och expertis för att undvika skador på företag och samhälle. Lokala språkmodeller eller API-leverantörer med högt förtroende rekommenderas för att minimera risken att känslig information läcker ut till en oönskad tredje part. Med stor försiktighet och hänsyn till riskerna kan ett effektivt kunskapsverktyg bidra till ökad effektivitet, snabbare och mer hållbar utveckling av kraftledningsinfrastruktur, och därmed en snabbare energiomställning.

This thesis explores how large language models can be used to generate configuration files for Terraform from natural language descriptions. Few-shot and fine-tuning paradigms are evaluated on decoder-only models of varying size, including the state-of-the-art Codex model. The generated configuration files are evaluated with regard to functional correctness on a custom dataset using Terraform, to account for the large space of functionally equivalent configuration files. Results show that the largest model Codex is very capable at generating configuration files given an English description of network infrastructure even without fine-tuning. The result could be a useful tool for engineers who know Terraform fundamentals and have experience with the cloud platforms: AWS, GCP, or Azure. A future study could fine-tune Codex for Terraform using OpenAI's API or create an open source Codex-replication by fine-tuning the GPT-3 replication OPT, which in turn can be \hbox{fine-tuned}. / Denna avhandling undersöker hur stora språkmodeller kan användas till att generera konfigurationsfiler för Terraform med hjälp av språkbeskrivningar. Både few-shot och fine-tuning paradigm utvärderas på decoder-only modeller i olika storlekar, inklusive Codex. För att ta hänsyn till konfigurationsfiler som i utseende ser olika ut men som är funktionellt ekvivalenta utvärderas konfigurationsfilerna utifrån deras funktion. Resultaten visar att Codex, som är den största modellen, har förmågan att generera konfigurationsfiler givet en engelsk beskrivning av nätverksinfrastruktur, trots att Codex inte har undergått fine-tuning. Resultatet kan vara ett användbart verktyg för ingenjörer som har grundläggande kunskap om Terraform och erfarenhet av molnplattformarna: AWS, GCP eller Azure. En framtida studie skulle kunna träna Codex för Terraform med OpenAI:s API eller skapa en Codex-kopia genom att träna GPT-3 kopian OPT som i sin tur kan bli tränad för Terraform.

The advancements in abstractive summarization using Large Language Models (LLMs) have brought with it new challenges in evaluating the quality and faithfulness of generated summaries. This thesis explores a human-like automated method for evaluating news article summaries. By leveraging two LLMs with instruction-following capabilities (GPT-4 and Claude), the aim is to examine to what extent the quality of summaries can be measured by predictions of an LLM. The proposed framework involves defining specific attributes of desired summaries, which are used to design generation prompts and evaluation questions. These questions are presented to the LLMs in natural language during evaluation to assess of various summary qualities. To validate the effectiveness of the evaluation method, an adversarial approach is employed, in which a dataset comprising summaries with distortions related to various summary attributes is generated. In an experiment, the two LLMs evaluate the adversarial dataset, and their ability to detect known distortions is measured and analyzed. The findings suggest that the LLM-based evaluations demonstrate promise in detecting binary qualitative issues, such as incorrect facts. However, the reliability of the zero-shot evaluation varies depending on the evaluating LLM and the specific questions used. Further research is required to validate the accuracy and generalizability of the results, particularly in subjective dimensions where the results of this thesis are inconclusive. Nonetheless, this thesis provides insights that can serve as a foundation for future advancements in the field of automatic text evaluation. / De framsteg som gjorts inom abstrakt sammanfattning med hjälp av stora språkmodeller (LLM) har medfört nya utmaningar när det gäller att utvärdera kvaliteten och sanningshalten hos genererade sammanfattningar. Detta examensarbete utforskar en mänskligt inspirerad automatiserad metod för att utvärdera sammanfattningar av nyhetsartiklar. Genom att dra nytta av två LLM:er med instruktionsföljande förmågor (GPT-4 och Claude) är målet att undersöka i vilken utsträckning kvaliteten av sammanfattningar kan bestämmas med hjälp av språkmodeller som utvärderare. Det föreslagna ramverket innefattar att definiera specifika egenskaper hos önskade sammanfattningar, vilka används för att utforma genereringsuppmaningar (prompts) och utvärderingsfrågor. Dessa frågor presenteras för språkmodellerna i naturligt språk under utvärderingen för att bedöma olika kvaliteter hos sammanfattningar. För att validera utvärderingsmetoden används ett kontradiktoriskt tillvägagångssätt där ett dataset som innefattar sammanfattningar med förvrängningar relaterade till olika sammanfattningsattribut genereras. I ett experiment utvärderar de två språkmodellerna de motstridiga sammanfattningar, och deras förmåga att upptäcka kända förvrängningar mäts och analyseras. Resultaten tyder på att språkmodellerna visar lovande resultat vid upptäckt av binära kvalitativa problem, såsom faktafel. Dock varierar tillförlitligheten hos utvärderingen beroende på vilken språkmodell som används och de specifika frågorna som ställs. Ytterligare forskning krävs för att validera tillförlitligheten och generaliserbarheten hos resultaten, särskilt när det gäller subjektiva dimensioner där resultaten är osäkra. Trots detta ger detta arbete insikter som kan utgöra en grund för framtida framsteg inom området för automatisk textutvärdering.

There is a widespread problem of cookie banners not being compliant with the General Data Protection Regulation (GDPR), which negatively impacts user experience and violates personal data rights. To mitigate this issue, strides need to be made in violation detection to assist developers, designers, lawyers, organizations, and authorities in designing and enforcing GDPR-compliant cookie banners. In this thesis, we present a novel method and an open-source tool for automatically analyzing the GDPR compliance of cookie banners. The tool uniquely leverages large language models together with static code analysis to locate and analyze any cookie banner, using only the website address as input. Informed by the Design Science Research methodology, our research process involved interviews with GDPR legal experts and a thorough review of current literature in order to understand the problem context and define the objectives for our solution. After an initial version of the tool was created, an evaluation was performed by a GDPR legal expert. The feedback revealed that even at this early development stage, the tool approaches the capabilities of a trained eye, which illustrates its potential. Furthermore, our proposed method is generalizable and can be used under many domains to solve various problems (e.g., more generalized web scraping). However, further development and testing with the help of legal experts is required to enhance the tool's accuracy and validity.

<p dir="ltr">Our ability to understand language and perform reasoning crucially relies on a robust system of semantic cognition (G. L. Murphy, 2002; Rogers & McClelland, 2004; Rips et al., 2012; Lake & Murphy, 2021): processes that allow us to learn, update, and produce inferences about everyday concepts (e.g., cat, chair), properties (e.g., has fur, can be sat on), categories (e.g., mammals, furniture), and relations (e.g., is-a, taller-than). Meanwhile, recent progress in the field of natural language processing (NLP) has led to the development of language models (LMs): sophisticated neural networks that are trained to predict words in context (Devlin et al., 2019; Radford et al., 2019; Brown et al., 2020), and as a result build representations that encode the knowledge present in the statistics of their training environment. These models have achieved impressive levels of performance on a range of tasks that require sophisticated semantic knowledge (e.g. question answering and natural language inference), often even reaching human parity. To what extent do LMs capture the nuances of human conceptual knowledge and reasoning? Centering around this broad question, this dissertation uses core ideas in human semantic cognition as guiding principles and lays down the groundwork to establish effective evaluation and improvement of conceptual understanding in LMs. In particular, I build on prior work that focuses on characterizing what semantic knowledge is made available in the behavior and representations of LMs, and extend it by additionally proposing tests that focus on functional consequences of acquiring basic semantic knowledge.<br><br>I primarily focus on inductive generalization (Hayes & Heit, 2018)—the unique ability of humans to rely on acquired conceptual knowledge to project or generalize novel information—as a context within which we can analyze LMs’ encoding of conceptual knowledge. I do this, since the literature surrounding inductive generalization contains a variety of empirical regularities that map to specific conceptual abstractions and shed light on how humans store, organize and use conceptual knowledge. Before explicitly analyzing LMs for these empirical regularities, I test them on two other contexts, which also feature the role of inductive generalization. First I test the extent to which LMs demonstrate typicality effects—a robust finding in human categorization literature where certain members of a category are considered to be more central to the category than are others. Specifically, I test the behavior 19 different LMs on two contexts where typicality effects modulate human behavior: 1) verification of sentences expressing taxonomic category membership, and 2) projecting novel properties from individual category members to the entire category. In both tests, LMs achieved positive but modest correlations with human typicality ratings, suggesting that they can to a non-trivial extent capture subtle differences between category members. Next, I propose a new benchmark to test the robustness of LMs in attributing properties to everyday concepts, and in making inductive leaps to endow properties to novel concepts. On testing 31 different LMs for these capacities, I find that while they can correctly attribute properties to everyday concepts and even predict the properties of novel concepts in simple settings, they struggle to do so robustly. Combined with the analyses of typicality effects, these results suggest that the ability of LMs to demonstrate impressive conceptual knowledge and reasoning behavior can be explained by their sensitivities to shallow predictive cues. When these cues are carefully controlled for, LMs show critical failures in demonstrating robust conceptual understanding. Finally, I develop a framework that can allow us to characterize the extent to which the distributed representations learned by LMs can encode principles and abstractions that characterize inductive behavior of humans. This framework operationalizes inductive generalization as the behavior of an LM after its representations have been partially exposed (via gradient-based learning) to novel conceptual information. To simulate this behavior, the framework uses LMs that are endowed with human-elicited property knowledge, by training them to evaluate the truth of sentences attributing properties to concepts. I apply this framework to test four different LMs on 13 different inductive phenomena documented for humans (Osherson et al., 1990; Heit & Rubinstein, 1994). Results from these analyses suggest that building representations from word distributions can successfully allow the encoding of many abstract principles that can guide inductive behavior in the models—principles such as sensitivity to conceptual similarity, hierarchical organization of categories, reasoning about category coverage, and sample size. At the same time, the tested models also systematically failed at demonstrating certain phenomena, showcasing their inability to demonstrate pragmatic reasoning, preference to rely on shallow statistical cues, and lack of context sensitivity with respect to high-level intuitive theories.</p>

This thesis explores the potential of Codex, a pre-trained Large Language Model (LLM), for Automated Program Repair (APR) by assessing its performance on the Defects4J benchmark that includes real-world Java bugs. The study aims to provide a comprehensive understanding of Codex’s capabilities and limitations in generating syntactically and semantically equivalent patches for defects, as well as evaluating its ability to handle defects with different levels of importance and complexity. Additionally, we aim to compare the performance of Codex with other LLMs in the APR domain. To achieve these objectives, we employ a systematic methodology that includes prompt engineering, Codex parameter adjustment, code extraction, patch verification, and Abstract Syntax Tree (AST) comparison. We successfully verified 528 bugs in Defects4J, which represents the highest number among other studies, and achieved 53.98% of plausible and 26.52% correct patches. Furthermore, we introduce the elle-elle-aime framework, which extends the RepairThemAll for Codex-based APR and is adaptable for evaluating other LLMs, such as ChatGPT and GPT-4. The findings of this empirical study provide valuable insights into the factors that impact Codex’s performance on APR, helping to create new prompt strategies and techniques that improve research productivity. / Denna avhandling utforskar potentialen hos Codex, en förtränad LLM, för APR genom att utvärdera dess prestanda på Defects4J-benchmarket som inkluderar verkliga Java-buggar. Studien syftar till att ge en omfattande förståelse för Codex förmågor och begränsningar när det gäller att generera syntaktiskt och semantiskt ekvivalenta patchar för defekter samt att utvärdera dess förmåga att hantera defekter med olika nivåer av betydelse och komplexitet. Dessutom är vårt mål att jämföra prestanda hos Codex med andra LLM inom APR-området. För att uppnå dessa mål använder vi en systematisk metodik som inkluderar prompt engineering, justering av Codex-parametrar, kodextraktion, patchverifiering och jämförelse av AST. Vi verifierade framgångsrikt 528 buggar i Defects4J, vilket representerar det högsta antalet bland andra studier, och uppnådde 53,98% plausibla och 26,52% korrekta patchar. Vidare introducerar vi elle-elle-aime ramverket, som utvidgar RepairThemAll för Codex-baserad APR och är anpassningsbart för att utvärdera andra LLM, såsom ChatGPT och GPT-4. Resultaten av denna empiriska studie ger värdefulla insikter i de faktorer som påverkar Codex prestanda på APR och hjälper till att skapa nya promptstrategier och tekniker som förbättrar forskningsproduktiviteten.

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.

Dealing with low-resource languages is a challenging task, because of the absence of sufficient data to train machine-learning models to make predictions on these languages. One way to deal with this problem is to use data from higher-resource languages, which enables the transfer of learning from these languages to the low-resource target ones. The present study focuses on dependency parsing and part-of-speech tagging of low-resource languages belonging to the spoken genre, i.e., languages whose treebank data is transcribed speech. These are the following: Beja, Chukchi, Komi-Zyrian, Frisian-Dutch, and Cantonese. Our approach involves investigating different types of transfer languages, employing MACHAMP, a state-of-the-art parser and tagger that uses contextualized word embeddings, mBERT, and XLM-R in particular. The main idea is to explore how the genre, the language similarity, none of the two, or the combination of those affect the model performance in the aforementioned downstream tasks for our selected target treebanks. Our findings suggest that in order to capture speech-specific dependency relations, we need to incorporate at least a few genre-matching source data, while language similarity-matching source data are a better candidate when the task at hand is part-of-speech tagging. We also explore the impact of multi-task learning in one of our proposed methods, but we observe minor differences in the model performance.

Broken Access Control is the most serious web application security risk as published by Open Worldwide Application Security Project (OWASP). This category has highly complex vulnerabilities such as Broken Object Level Authorization (BOLA) and Exposure of Sensitive Information. Finding such critical vulnerabilities in large software systems requires intelligent and automated tools. State-of-the-art (SOTA) research including hybrid application security testing tools, algorithmic brute forcers, and artificial intelligence has shown great promise in detection. Nevertheless, there exists a gap in research for reliably identifying logical and context-dependant Broken Access Control vulnerabilities. We modeled the problem as text classification and proposed KARTAL, a novel method for web application vulnerability detection using a Large Language Model (LLM). It consists of 3 components: Fuzzer, Prompter, and Detector. The Fuzzer is responsible for methodically collecting application behavior. The Prompter processes the data from the Fuzzer and formulates a prompt. Finally, the Detector uses an LLM which we have finetuned for detecting vulnerabilities. In the study, we investigate the performance, key factors, and limitations of the proposed method. Our research reveals the need for a labeled Broken Access Control vulnerability dataset in the cybersecurity field. Thus, we custom-generate our own dataset using an auto-regressive LLM with SOTA few-shot prompting techniques. We experiment with finetuning 3 types of decoder-only pre-trained transformers for detecting 2 sophisticated vulnerabilities. Our best model attained an accuracy of 87.19%, with an F1 score of 0.82. By using hardware acceleration on a consumer-grade laptop, our fastest model can make up to 539 predictions per second. The experiments on varying the training sample size demonstrated the great learning capabilities of our model. Every 400 samples added to training resulted in an average MCC score improvement of 19.58%. Furthermore, the dynamic properties of KARTAL enable inferencetime adaption to the application domain, resulting in reduced false positives. / Brutet åtkomstkontroll är den allvarligaste säkerhetsrisken för webbapplikationer enligt Open Worldwide Application Security Project (OWASP). Denna kategori har mycket komplexa sårbarheter såsom Brutet behörighetskontroll på objektnivå (BOLA) och exponering av känslig information. Att hitta sådana kritiska sårbarheter i stora programvarusystem kräver intelligenta och automatiserade verktyg. Senaste tekniken (SOTA)-forskning, inklusive hybridverktyg för säkerhetstestning av applikationer, algoritmiska bruteforcers och artificiell intelligens, har visat stor potential för upptäckt. Trots detta finns det en lucka i forskningen när det gäller tillförlitlig identifiering av logiska och kontextberoende sårbarheter relaterade till Brutet åtkomstkontroll. Vi modellerade problemet som textklassificering och föreslog KARTAL, en ny metod för att upptäcka sårbarheter i webbapplikationer med hjälp av en stor språkmodell (LLM). Den består av 3 komponenter: Fuzzer, Prompter och Detector. Fuzzer ansvarar för att systematiskt samla in applikationsbeteende. Prompter bearbetar data från Fuzzer och formulerar en förfrågan. Slutligen använder Detector en LLM som vi har finjusterat för att upptäcka sårbarheter. I studien undersöker vi prestanda, nyckelfaktorer och begränsningar hos den föreslagna metoden. Vår forskning visar behovet av en märkt dataset för sårbarheter relaterade till Brutet åtkomstkontroll inom cybersäkerhetsområdet. Därför genererar vi anpassade dataset med hjälp av en auto-regressiv LLM med SOTA few-shot-prompting-tekniker. Vi experimenterar med att finjustera 3 typer av endast avkodare transformers som är förtränade för att upptäcka 2 sofistikerade sårbarheter. Vår bästa modell uppnådde en noggrannhet på 87.19% med en F1-poäng på 0.82. Genom att använda hårdvaruacceleration på en bärbar dator för konsumenter kan vår snabbaste modell göra upp till 539 förutsägelser per sekund. Experimenten med varierande storlek på träningsprovet visade på vår modells stora förmåga att lära sig. Varje 400 prover som lades till träningen resulterade i en genomsnittlig förbättring av MCC-poängen med 19.58%. Dessutom möjliggör de dynamiska egenskaperna hos KARTAL anpassning vid inferringstid till applikationsdomänen, vilket resulterar i färre falska positiva resultat.

Previous works in Human-Robot Interaction have demonstrated the positive potential benefit of designing highly anthropomorphic robots. This includes physical appearance but also whether they can express emotions, behave in a congruent manner, etc. This work wants to explore the creation of a robot that is able to express a given personality consistently throughout a dialogue while also manifesting congruent emotional expressions. Personality defines many aspects of the character of a person and it can influence how one speaks, behaves, reacts to events, etc. Here, we only focus our attention on language and on how it changes depending on one particular personality trait, the extraversion. To this end, we tested different language models to automate the process of generating language according to a particular personality. We also compared large language models such as GPT-3 to smaller ones, to analyse how size can correlate to performance in this task. We initially evaluated these methods through a fairly small user study in order to confirm the correct manipulation of personality in a text-only context. Results suggest that personality manipulation and how well it is understood highly depend on the context of a dialogue, with a more ‘personal’ dialogue being more successful in manifesting personality. Also, the performance of GPT-3 is comparable to smaller models, specifically trained, with the main difference only given in the perceived fluency of the generations. We then conducted a follow-up study where we chose to use a robot that is capable of showing different facial expressions used to manifest different emotions, the Furhat robot. We integrated into the robot the generations from our language models together with an emotion classification method that is used to guide its facial expressions. Whilst the output of our models did trigger different emotional expressions, resulting in robots which differed both in their language and nonverbal behaviour, resultant perception of these robots’ personality only approached significance (p ∼ 0.08). In this study, GPT3 performed very similarly to much smaller models, with the difference in fluency also being much smaller than before. We did not see any particular change in the perception of the robots in terms of likeability nor uncanniness. / Tidigare arbeten inom Människa-robotinteraktion har visat den positiva potentiella fördelen med att designa mycket antropomorfa robotar. Detta inkluderar fysiskt utseende men också huruvida de kan uttrycka känslor, bete sig på ett kongruent sätt, etc. Detta arbete vill utforska skapandet av en robot som kan uttrycka en given personlighet konsekvent under en dialog samtidigt som den manifesterar kongruenta känslomässiga uttryck. Personlighet definierar många aspekter av en persons karaktär och den kan påverka hur man talar, beter sig, reagerar på händelser etc. Här fokuserar vi vår uppmärksamhet endast på språket och på hur det förändras beroende på ett särskilt personlighetsdrag, extraversion. För detta ändamål testade vi olika språkmodeller för att automatisera processen att skapa språk enligt en viss personlighet. Vi jämförde även stora språkmodeller som GPT-3 med mindre, för att analysera hur storlek kan relatera till prestanda i denna uppgift. Vi utvärderade inledningsvis dessa metoder genom en mindre användarstudie för att bekräfta att personligheten kan manipuleras på rätt sätt i en textbaserad kontext. Resultaten tyder på att personlighetsmanipulation och hur väl den förstås i hög grad beror på sammanhanget i en dialog, där en mer ‘personlig’ dialog är mer framgångsrik när det gäller att manifestera personlighet. Prestandan hos GPT-3 är också jämförbar med mindre modeller, specifikt tränade på en uppgift, där den största skillnaden var i den genererade textens upplevda flyt. Vi gjorde sedan en uppföljningsstudie där vi valde att använda en robot som är kapabel att visa olika ansiktsuttryck och därigenom kapabel att manifestera olika känslor, Furhat-roboten. Vi integrerade talet som genererades från våra språkmodeller i roboten tillsammans med en känsloklassificeringsmetod som används för att styra dess ansiktsuttryck. Medan resultatet av våra modeller framkallade olika känslomässiga uttryck, vilket resulterade i robotar som skilde sig åt både i språk och icke-verbal kommunikation, närmade sig endast den resulterande uppfattningen av dessa robotars personlighet signifikans (p ∼ 0.08). I denna studie presterade GPT-3 mycket likartat med mycket mindre modeller, med skillnaden i flyt också mycket mindre än tidigare. Vi såg ingen speciell förändring i uppfattningen av robotarna när det gäller sympati eller obehaglighet.