The engineering design process can underutilize nature-based solutions during infrastructure development. Instances of nature within the built environment are reflections of the human-nature connection, which may alter how designers ideate solutions to a given design task, especially through virtual reality (VR) as an embodied perspective taking platform. Embodied VR helps designers "see" as an end-user sees, inclusive of the natural environment through the uptake of an avatar, such as a bird or fish. Embodied VR emits empathy toward the avatar, e.g., to see as a bird in VR, one tends to feel and think as a bird. Furthermore, embodied VR also impacts altruistic behavior toward the environment, specifically through proenvironmental behaviors. However, limited research discovers the impact of embodied VR on the human-nature connection and if embodied VR has any impact on how designers ideate, specifically surrounding nature-based solutions as a form of a proenvironmental behavior during the design process. This research first presents a formal measurement of embodied VR's impact on the human-nature connection and maps this impact toward design-related proenvironmental behaviors through design ideas, i. e., tracking changes in nature-based design choices. The design study consisted of three groups of engineering undergraduate students which were given a case study and plan review: a VR group embodying a bird (n=35), a self-lens VR group (n=34), and a control group (n=33). The case study was about a federal mandate to minimize combined sewer overflow in a neighborhood within Cincinnati, OH. Following the plan review, VR groups were given a VR walkthrough or flythrough of the case study area of interest as a selected avatar (embodied:bird, self-lens:oneself). Participants were tested for their connectedness to nature and a mock-design charrette was held to measure engineering design ideas. Verbal protocol analysis was followed, instructing participants to think aloud. Design ideation sessions were recorded and manually transcribed. The results of the study indicated that embodiment impacts the human-nature connection based on participants' perceived connection to nature. Only the bird group witnessed an increase in connectedness to nature, whereas the self-lens and control groups did not report any change. This change in connectedness to nature was also confirmed by engineering design ideas. The bird group was more likely to ideate green-thinking designs to solve the stormwater issue and benefit both nature and socioeconomic conditions, whereas the control group mostly discussed gray designs as the catalyst for minimizing combined sewer overflows. The self-lens group also mentioned green design ideas as well as socioeconomic change, but mostly placed the beneficiary of the design toward people rather than nature in the bird group. The mode of analysis for these findings was driven by thematic content analysis, an exploration of design space as a function of semantic distance, and large language models (LLMs) to synthesize design ideas and themes. An LLM's performance lent accuracy to the design ideas in comparison to thematic content analysis, but struggled to cross-compare groups to provide generalizable findings. This research is intended to benefit the engineering design process with a) the benefit of perspective-taking on design ideas based on lenses of embodied VR and b) various methods to supplement thematic content analysis for coding design ideas. / Doctor of Philosophy / The use of nature in the constructed world, such as rain gardens and natural streams for moving stormwater, is underused during the design process. Virtual reality (VR) programs, like embodiment, have the potential to increase the incorporation of nature and nature-based elements during design. Embodiment is the process of taking on the vantage point of another being or avatar, such as a bird, fish, insect, or other being, in order to see and move as the avatar does. Embodied VR increases the likelihood that the VR participant will act favorably to the subject, specifically when the natural environment is involved. For example, embodying another individual cutting down trees in a virtual forest increased the likelihood that individuals would act favorably to the environment, such as through recycling or conserving energy (Ahn and Bailenson, 2012). Ultimately, this research measures the level of connection participants feel with the environment after an embodied VR experience and motions to discover if this change in connection to nature impacts how participants might design a solution to a problem. This design experiment is based on a case study, which all participants were provided alongside supplemental plan documents of the case. The case study used is about stormwater issues and overflows from infrastructure in a neighborhood in Cincinnati, OH, where key decision-makers were mandated by the federal government to minimize the overflows. The bird group (a bird avatar) performed a fly-through in the area of interest in VR, whereas the self-lens group (first-person, embodying oneself) walked through the same area. The control group received no VR intervention. Following the intervention, participants were asked to re-design the neighborhood and orate their recorded solution. Then, participants were required to score a questionnaire measuring their connectedness to nature. The results show that when people experience the space as a bird in virtual reality, they felt more connected to nature and also included more ideas related to nature in their design. More specifically, ideas involving green infrastructure (using nature-based elements, e.g., rain gardens and streams) and socioeconomic benefits were brought up by the bird group. This research presents embodiment as a tool that can change how engineers design. As stormwater policy has called for more use of green infrastructure (notably, through the Environmental Protection Agency), embodiment may be used during the design process to meet this call from governmental programs. Furthermore, this research impacts how embodiment's effects on design can be interpreted, specifically through quantitative methods through natural language processing and the use of large language models to analyze data and report back on design-related findings. This research is intended to benefit the design process with a) using different avatars in embodiment to impact design ideas and b) a comparison of thematic content analysis and large language models in summarizing design ideas and themes.

This thesis aims to explore and gain an understanding of the current AI landscape within Swedish Cultural Heritage using purposive interviews with five cultural heritage institutions with ongoing AI projects. This study fills a knowledge gap in the practical implementation of AI at Swedish institutions in addition to the sustainable use of technologies for cultural heritage. The overarching discussion further includes related topics of ethical AI and long-term sustainability, framing it from a perspective of Information Practices and a socio-material entanglement. Findings show that AI technologies can play an important part in cultural heritage, with a range of practical applications if certain issues are overcome. Moreover, the utilisation of AI will increase. The study also indicates a need for regulations, digitisation efforts, and increased investments in resources to adopt the technologies into current practices sustainably. The conclusion highlights a need for the cultural heritage sector to converge and find collectively applicable solutions for implementing AI.

The field of AI and machine learning has seen exponential growth in the last decade and even more so in the recent year with the considerable public interest in Large Language models (LLMs) such as chat-GPT. LLMs can be used for several purposes, but one possible application would be fine-tuning a model to perform a particular function in a specific field. The goal is therefore fine-tuning a LLM in the field of psychology using a new method called Reinforcement Learning from Human Feedback to determine if it is a viable method in such cases. The theory behind LLMs and RLHF as well as the ethical perspective on developing a psychological AI is presented. Previous studies on both RLHF and AI in psychology are presented, showing the goal is feasible. Then the method is explained for both training and evaluating the model which is done by comparing a pre-trained model with the fine-tuned one. The study is considered scientifically relevant as RLHF has been used to fine-tune LLMs earlier, but has not been done with the intent to make it more specified in a field. The result did not show any clear difference between the pre-trained and the fine-tuned model therefore, more tests are required. However, with the limitations regarding hardware, time to train, and available data, there is much improvement needed for future studies. An ethical framework applied to a digital psychology assistant is discussed and a suitable introduction to the market and division of responsibilities is proposed. / Området AI och maskininlärning har sett exponentiell tillväxt under det senaste decenniet och ännu mer under det senaste året med det stora allmänintresset för stora språkmodeller som chat-GPT. Stora språkmodeller kan användas till flera saker där en möjlig tillämpning är att finjustera en modell för att fylla en viss funktion inom ett specifikt yrke. Målet med arbetet är därför att finjustera en språkmodell inom området psykologi med hjälp av en ny metod kallad Reinforcement Learning from Human Feedback för att undersöka metodens tillämplighet. Teorin bakom stora språkmodeller och RLHF samt det etiska perspektivet på att utveckla en digital psykologi assistent förklaras. Därefter presenteras tidigare studier om både RLHF och AI inom psykologi som visar att målet är genomförbart. Metoden för att både träna och utvärdera modellen förklaras som görs genom att jämföra den förtränade modellen med den finjusterade. Studien bedöms som vetenskapligt relevant även fast RLHF har använts för att finjustera språkmodeller tidigare, har det inte gjorts med målet att finjustera en språkmodell till ett visst yrke. Resultatet visade inte på någon tydlig skillnad mellan den förtränade och den finjusterade modellen, därför krävs fler tester krävs. Men med de begräsningar som fanns gällande hårdvara, tid att träna och tillgänglig data är det mycket som kan förbättras i framtida studier. Det etiska ramverket applicerat på en digital psykologi assistent diskuteras och en lämplig introduktion till marknaden och ansvarsfördelning föreslås.

This study investigates the utilization of Large Language Models for Test Case Generation. The study uses the Large Language model and Embedding model provided by Llama, specifically Llama2 of size 7B, to generate test cases given a defined input. The study involves an implementation that uses customization techniques called Retrieval Augmented Generation (RAG) and Prompt Engineering. RAG is a method that in this study, stores organisation information locally, which is used to create test cases. This stored data is used as complementary data apart from the pre-trained data that the large language model has already trained on. By using this method, the implementation can gather specific organisation data and therefore have a greater understanding of the required domains. The objective of the study is to investigate how AI-driven test case generation impacts the overall software quality and development efficiency. This is evaluated by comparing the output of the AI-based system, to manually created test cases, as this is the company standard at the time of the study. The AI-driven test cases are analyzed mainly in the form of coverage and time, meaning that we compare to which degree the AI system can generate test cases compared to the manually created test case. Likewise, time is taken into consideration to understand how the development efficiency is affected. The results reveal that by using Retrieval Augmented Generationin combination with Prompt Engineering, the system is able to identify test cases to a certain degree. The results show that 66.67% of a specific project was identified using the AI, however, minor noise could appear and results might differ depending on the project’s complexity. Overall the results revealed how the system can positively impact the development efficiency and could also be argued to have a positive effect on the software quality. However, it is important to understand that the implementation as its current stage, is not sufficient enough to be used independently, but should rather be used as a tool to more efficiently create test cases.

The development of artificial intelligence (AI) has revolutionised the way we interact with technology and each other, both in society and in professional careers. Although they come with great potential for productivity and automation, AI systems have been found to exhibit biases that reflect and perpetuate existing societal inequalities. With the recent rise of artificial intelligence tools exploiting the large language model (LLM) technology, such as ChatGPT, Bing Chat and Bard AI, this research project aims to investigate the extent of AI bias in said tools and explore its ethical implications. By reviewing and analysing responses to carefully crafted prompts generated by three different AI chatbot tools, the author will intend to determine whether the content generated by these tools indeed exhibits patterns of bias related to various social identities, as well as compare the extent to which such bias is present across all three tools. This study will contribute to the growing body of literature on AI ethics and inform efforts to develop more equitable and inclusive AI systems. By exploring the ethical dimensions of AI bias in selected LLMs, this research will shed light on the broader societal implications of AI and the role of technology in shaping our future.

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.

Large Language Models (LLMs) have become ever larger in recent years, typically demonstrating improved performance as the number of parameters increases. This thesis investigates how the probability distributions output by language models differ depending on the size of the model. For this purpose, three features for capturing the differences between the distributions are defined, namely the difference in entropy, the difference in probability mass in different slices of the distribution, and the difference in the number of tokens covering the top-p probability mass. The distributions are then put into different distribution classes based on how they differ from the distributions of the differently-sized model. Finally, the distributions are transformed to be more similar to the distributions of the other model. The results suggest that classifying distributions before transforming them, and adapting the transformations based on which class a distribution is in, improves the transformation results. It is also shown that letting a classifier choose the class label for each distribution yields better results than using random labels. Furthermore, the findings indicate that transforming the distributions using entropy and the number of tokens in the top-p probability mass makes the distributions more similar to the targets, while transforming them based on the probability mass of individual slices of the distributions makes the distributions more dissimilar.