[pt] Neste trabalho, apresentamos um estudo sobre o fine-tuning de um LLM (Modelo de Linguagem Amplo ou Large Language Model) pré-treinado para a sumarização abstrativa de textos longos em português. Para isso, construímos um corpus contendo uma coleção de 7.450 artigos científicos na área de Ciências da Saúde em português. Utilizamos esse corpus para o fine-tuning do modelo BERT pré-treinado para o português brasileiro (BERTimbau). Em condições semelhantes, também treinamos um segundo modelo baseado em Memória de Longo Prazo e Recorrência (LSTM) do zero, para fins de comparação. Nossa avaliação mostrou que o modelo ajustado obteve pontuações ROUGE mais altas, superando o modelo baseado em LSTM em 30 pontos no F1-score. O fine-tuning do modelo pré-treinado também se destaca em uma avaliação qualitativa feita por avaliadores a ponto de gerar a percepção de que os resumos gerados poderiam ter sido criados por humanos em uma coleção de documentos específicos do domínio das Ciências da Saúde. / [en] In this work, we present a study on the fine-tuning of a pre-trained Large Language Model for abstractive summarization of long texts in Portuguese. To do so, we built a corpus gathering a collection of 7,450 public Health Sciences papers in Portuguese. We fine-tuned a pre-trained BERT model for Brazilian Portuguese (the BERTimbau) with this corpus. In a similar condition, we also trained a second model based on Long Short-Term Memory (LSTM) from scratch for comparison purposes. Our evaluation showed that the fine-tuned model achieved higher ROUGE scores, outperforming the LSTM based by 30 points for F1-score. The fine-tuning of the pre-trained model also stands out in a qualitative evaluation performed by assessors, to the point of generating the perception that the generated summaries could have been created by humans in a specific collection of documents in the Health Sciences domain.

The release of ChatGPT has really set new standards for what an artificial intelligence chatbot should be. It has even shown its potential in answering university-level exam questions from different subjects. This research is focused on evaluating its capabilities in programming subjects. To achieve this, coding questions taken from software engineering exams were posed to the AI (N = 23) through an experiment. Then, statistical analysis was done to find out how good of a student ChatGPT is by analyzing its answer’s correctness, degree of completion, diversity of response, speed of response, extraneity, number of errors, length of response and confidence levels. GPT-3.5 is the version analyzed. The experiment was done using questions from three different programming subjects. Afterwards, results showed a 93% rate of correct answer generation, demonstrating its competence. However, it was found that the AI occasionally produces unnecessary lines of code that were not asked for and thus treated as extraneity. The confidence levels given by ChatGPT, which were always high, also didn't always align with response quality which showed the subjectiveness of the AI’s self-assessment. Answer diversity was also a concern, where most answers were repeatedly written nearly the same way. Moreover, when there was diversity in the answers, it also caused much more extraneous code. If ChatGPT was to be blind tested for a software engineering exam containing a good number of coding questions, unnecessary lines of code and comments could be what gives it away as being an AI. Nonetheless, ChatGPT was found to have great potential as a learning tool. It can offer explanations, debugging help, and coding guidance just as any other tool or person could. It is not perfect though, so it should be used with caution.

This dissertation explores the use of natural language processing (NLP) and large language models (LLMs) to analyze student self-regulated learning (SRL) strategies in response to exam wrappers. Exam wrappers are structured reflection activities that prompt students to practice SRL after they get their graded exams back. The dissertation consists of three manuscripts that compare traditional qualitative analysis with NLP-assisted approaches using transformer-based models including GPT-3.5, a state-of-the-art LLM. The data set comprises 3,800 student responses from an engineering physics course. The first manuscript develops two NLP-assisted codebooks for identifying learning strategies related to SRL in exam wrapper responses and evaluates the agreement between them and traditional qualitative analysis. The second manuscript applies a novel NLP technique called zero-shot learning (ZSL) to classify student responses into the codes developed in the first manuscript and assesses the accuracy of this method by evaluating a subset of the full dataset. The third manuscript identifies the distribution and differences of learning strategies and SRL constructs among students of different exam performance profiles using the results from the second manuscript. The dissertation demonstrates the potential of NLP and LLMs to enhance qualitative research by providing scalable, robust, and efficient methods for analyzing large corpora of textual data. The dissertation also contributes to the understanding of SRL in engineering education by revealing the common learning strategies, impediments, and SRL constructs that students report they use while preparing for exams in a first-year engineering physics course. The dissertation suggests implications, limitations, and directions for future research on NLP, LLMs, and SRL. / Doctor of Philosophy / This dissertation is about using artificial intelligence (AI) to help researchers and teachers understand how students learn from their exams. Exams are not only a way to measure what students know, but also a chance for students to reflect on how they studied and what they can do better next time. One way that students can reflect is by using exam wrappers, which are short questions that students answer after they get their graded exams back. A type of AI called natural language processing (NLP) is used in this dissertation, which can analyze text and find patterns and meanings in it. This study also uses a powerful AI tool called GPT-3.5, which can generate text and answer questions. The dissertation has three manuscripts that compare the traditional way of analyzing exam wrappers, which is done by hand, with the new way of using NLP and GPT-3.5, evaluate a specific promising NLP method, and use this method to try and gain a deeper understanding in students self-regulated learning (SRL) while preparing for exams. The data comes from 3,800 exam wrappers from a physics course for engineering students. The first manuscript develops a way of using NLP and GPT-3.5 to find out what learning strategies and goals students talk about in their exam wrappers and compares it to more traditional methods of analysis. The second manuscript tests how accurate a specific NLP technique is in finding these strategies and goals. The third manuscript looks at how different students use different strategies and goals depending on how well they did on the exams using the NLP technique in the second manuscript. I found that NLP and GPT-3.5 can aid in analyzing exam wrappers faster and provide nuanced insights when compared with manual approaches. The dissertation also shows what learning strategies and goals are most discussed for engineering students as they prepare for exams. The dissertation gives some suggestions, challenges, and ideas for future research on AI and learning from exams.

<p dir="ltr">Sexual harassment is regarded as a serious issue in society, with a particularly negative impact on young children and adolescents. Online sexual harassment has recently gained prominence as a significant number of communications have taken place online. Online sexual harassment can happen anywhere in the world because of the global nature of the internet, which transcends geographical barriers and allows people to communicate electronically. Online sexual harassment can occur in a wide variety of environments such as through work mail or chat apps in the workplace, on social media, in online communities, and in games (Chawki & El Shazly, 2013).<br>However, especially for non-native English speakers, due to cultural differences and language barriers, may vary in their understanding or interpretation of text-based sexual harassment (Welsh, Carr, MacQuarrie, & Huntley, 2006). To bridge this gap, previous studies have proposed large language models to detect and classify online sexual harassment, prompting a need to explore how language models comprehend the nuanced aspects of sexual harassment data. Prior to exploring the role of language models, it is critical to recognize the current gaps in knowledge that these models could potentially address in order to comprehend and interpret the complex nature of sexual harassment.</p><p><br></p><p dir="ltr">The Large Language Model (LLM) has attracted significant attention recently due to its exceptional performance on a broad spectrum of tasks. However, these models are characterized by being very sensitive to input data (Fujita et al., 2022; Wei, Wang, et al., 2022). Thus, the purpose of this study is to examine how various LLMs interpret data that falls under the domain of sexual harassment and how they comprehend it after replacing Out-of-Vocabulary words.</p><p dir="ltr"><br>This research examines the impact of Out-of-Vocabulary words on the performance of LLMs in classifying sexual harassment behaviors in text. The study compares the story classification abilities of cutting-edge LLM, before and after the replacement of Out-of-Vocabulary words. Through this investigation, the study provides insights into the flexibility and contextual awareness of LLMs when managing delicate narratives in the context of sexual harassment stories as well as raises awareness of sensitive social issues.</p>

<p dir="ltr">This research explores the application of large language models (LLMs) to comprehension of puns. Leveraging the expansive capabilities of LLMs, this study delves into the domain of pun classification by examining it through the prism of two humor theories: the Computational Model of Humor and the Benign Violation theory, which is an extension of the N+V Theory. The computational model posits that for a phrase to qualify as a pun, it must possess both ambiguity and distinctiveness, characterized by a word that can be interpreted in two plausible ways, each interpretation being supported by at least one unique word. On the other hand, the Benign Violation theory posits that puns work by breaching one linguistic rule while conforming to another, thereby creating a "benign violation." By leveraging the capabilities of large language models (LLMs), this research endeavors to scrutinize a curated collection of English language puns. Our aim is to assess the validity and effectiveness of the use of these theoretical frameworks in accurately classifying puns. We undertake controlled experiments on the dataset, selectively removing a condition specific to one theory and then evaluating the puns based on the criteria of the other theory to see how well it classifies the altered inputs. This approach allows us to uncover deeper insights into the processes that facilitate the recognition of puns and to explore the practical implications of applying humor theories. The findings of our experiments, detailed in the subsequent sections, sheds light on how the alteration of specific conditions impacts the ability of the LLMs to accurately classify puns, according to each theory, where each component of the theory does not influence the result to the same extent, thereby contributing to our understanding of humor mechanics through the eyes of LLMs.</p>

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.

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.

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.