<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>

Användningen av Artificiell Intelligens (AI) är utbredd bland verksamma företag idag, likväl privatpersoner. Det har blivit en integrerad del av vårt samhälle som ofta går obemärkt förbi. Allt från face recognition, självkörande bilar och automatisering inom arbetsrelaterade områden, har AI onekligen påverkat omvärlden. I takt med att AI-modeller fortsätter att utvecklas tillkommer även farhågor om dess påverkan på jobb, tillhörande säkerhetsrisker och etiska dilemman. Uppsatsens litteratur hjälper till att skildra AI historiskt, i nutid, men även ge en uppfattning om vart den är på väg. Den AI-modell som i nuläget har väckt störst uppmärksamhet är ChatGPT. Dess potential tycks inte ha några gränser, därmed uppstod relevansen för att öka kunskapen kring AI-modellen. Vidare gjordes en avgränsning, där fokusområdet var att undersöka hur ChatGPT kan generera kodkommentarer och potentiellt agera som ett hjälpmedel vid kommentering av källkod. I samband med avgränsningen och fokusområdet bildades även forskningsfrågan: Large Language Models: Bedömning av ChatGPT:s potential som verktyg för kommentering av kod För att besvara forskningsfrågan har avhandlingen varit baserat på en kvalitativ ansats, där urvalet av respondenter har varit programmerare. Den primära datainsamlingen har genomförts via två semistrukturerade intervjuer, varav den inledande innefattade initiala känslor kring ChatGPT och övergripande fakta om respektive intervjuobjekt. Vidare gjordes det en observation för att få en inblick i hur AI-modellen används av programmerare, för att avslutningsvis göra en uppföljande intervju post-observation i syfte att samla tankarna från intervjuobjekten efter användning av ChatGPT för att generera kodkommentarer. Baserat på den insamlade empirin kunde studien konkludera vissa begränsningar i den nuvarande modellen, inte minst behovet av tydliga instruktioner. Trots brister visar ChatGPTs framställning potential att vara en betydande resurs för kommentering av kod i framtiden. Resultaten indikerar att modellen kan generera relativt passande kommentarer i de analyserade kodkodstycken. Emellertid uttryckte deltagarna under de avslutande intervjuerna generellt sett att kommentarerna var redundanta och saknade betydande värde för att öka förståelsen av källkoden. Respondenterna diskuterade dock möjligheterna att använda ChatGPT i framtiden, men underströk behovet av förbättringar för att göra det till en tillförlitlig metod inom arbetsrelaterade situationer. / The usage of Artificial Intelligence (AI) is widespread among both companies and individuals today. It has become an integrated part of our society, often going unnoticed. From face recognition and self-driving cars to automation in work-related areas, AI has undeniably impacted the world. As AI models continue to evolve, concerns about their impact on jobs, associated security risks, and ethical dilemmas arise. The literature in this essay helps portray AI historically, in the present, and provides an insight into its future direction. The AI model that has currently garnered the most attention is ChatGPT. Its potential seems limitless, which prompted the relevance of increasing knowledge about the AI model. Furthermore, a delimitation was made, where the focus area was to investigate how ChatGPT can generate code comments and potentially act as a tool for commenting source code. As part of the research focus and scope, the research question was formulated: "Large Language Models: Assessment of ChatGPT's Potential as a Tool for Code Commenting." To answer the research question, the thesis adopted a qualitative approach, with programmers as the selected respondents. The primary data collection was conducted through two semi-structured interviews, where the initial interview involved capturing initial impressions of ChatGPT and gathering general information about the interviewees. Additionally, an observation was carried out to gain insights into how programmers utilize the AI model, followed by a post-observation interview to gather the interviewees' thoughts after using ChatGPT to generate code comments. Based on the collected empirical data, the study was able to conclude certain limitations in the current model, particularly the need for clear instructions. Despite these limitations, ChatGPT's performance demonstrates the potential to be a significant resource for code commenting in the future. The results indicate that the model can generate relatively suitable comments in the analyzed code snippets. However, during the concluding interviews, participants generally expressed that the comments were redundant and lacked significant value in enhancing the understanding of the source code. Nevertheless, the respondents 2 discussed the possibilities of using ChatGPT in the future, while emphasizing the need for improvements to establish it as a reliable method in work-related situations.

Tesis por compendio / [ES] El advenimiento de los Large Language Models (LLMs) marca una fase transformadora en el campo de la Inteligencia Artificial (IA), significando el cambio hacia sistemas inteligentes y autónomos capaces de una comprensión y toma de decisiones complejas. Esta tesis profundiza en las capacidades multifacéticas de los LLMs, explorando sus posibles aplicaciones en la optimización de decisiones, la comprensión de escenas y tareas avanzadas de resumen de video en diversos contextos. En el primer segmento de la tesis, el foco está en la comprensión semántica de escenas de Vehículos Aéreos No Tripulados (UAVs). La capacidad de proporcionar instantáneamente datos de alto nivel y señales visuales sitúa a los UAVs como plataformas ideales para realizar tareas complejas. El trabajo combina el potencial de los LLMs, los Visual Language Models (VLMs), y los sistemas de detección objetos de última generación para ofrecer descripciones de escenas matizadas y contextualmente precisas. Se presenta una implementación práctica eficiente y bien controlada usando microdrones en entornos complejos, complementando el estudio con métricas de legibilidad estandarizadas propuestas para medir la calidad de las descripciones mejoradas por los LLMs. Estos avances podrían impactar significativamente en sectores como el cine, la publicidad y los parques temáticos, mejorando las experiencias de los usuarios de manera exponencial. El segundo segmento arroja luz sobre el problema cada vez más crucial de la toma de decisiones bajo incertidumbre. Utilizando el problema de Multi-Armed Bandits (MAB) como base, el estudio explora el uso de los LLMs para informar y guiar estrategias en entornos dinámicos. Se postula que el poder predictivo de los LLMs puede ayudar a elegir el equilibrio correcto entre exploración y explotación basado en el estado actual del sistema. A través de pruebas rigurosas, la estrategia informada por los LLMs propuesta demuestra su adaptabilidad y su rendimiento competitivo frente a las estrategias convencionales. A continuación, la investigación se centra en el estudio de las evaluaciones de bondad de ajuste de las Generative Adversarial Networks (GANs) utilizando la Signature Transform. Al proporcionar una medida eficiente de similitud entre las distribuciones de imágenes, el estudio arroja luz sobre la estructura intrínseca de las muestras generadas por los GANs. Un análisis exhaustivo utilizando medidas estadísticas como las pruebas de Kruskal-Wallis proporciona una comprensión más amplia de la convergencia de los GANs y la bondad de ajuste. En la sección final, la tesis introduce un nuevo benchmark para la síntesis automática de vídeos, enfatizando la integración armoniosa de los LLMs y la Signature Transform. Se propone un enfoque innovador basado en los componentes armónicos capturados por la Signature Transform. Las medidas son evaluadas extensivamente, demostrando ofrecer una precisión convincente que se correlaciona bien con el concepto humano de un buen resumen. Este trabajo de investigación establece a los LLMs como herramientas poderosas para abordar tareas complejas en diversos dominios, redefiniendo la optimización de decisiones, la comprensión de escenas y las tareas de resumen de video. No solo establece nuevos postulados en las aplicaciones de los LLMs, sino que también establece la dirección para futuros trabajos en este emocionante y rápidamente evolucionante campo. / [CA] L'adveniment dels Large Language Models (LLMs) marca una fase transformadora en el camp de la Intel·ligència Artificial (IA), significat el canvi cap a sistemes intel·ligents i autònoms capaços d'una comprensió i presa de decisions complexes. Aquesta tesi profunditza en les capacitats multifacètiques dels LLMs, explorant les seues possibles aplicacions en l'optimització de decisions, la comprensió d'escenes i tasques avançades de resum de vídeo en diversos contexts. En el primer segment de la tesi, el focus està en la comprensió semàntica d'escenes de Vehicles Aeris No Tripulats (UAVs). La capacitat de proporcionar instantàniament dades d'alt nivell i senyals visuals situa els UAVs com a plataformes ideals per a realitzar tasques complexes. El treball combina el potencial dels LLMs, els Visual Language Models (VLMs), i els sistemes de detecció d'objectes d'última generació per a oferir descripcions d'escenes matisades i contextualment precises. Es presenta una implementació pràctica eficient i ben controlada usant microdrons en entorns complexos, complementant l'estudi amb mètriques de llegibilitat estandarditzades proposades per a mesurar la qualitat de les descripcions millorades pels LLMs. Aquests avenços podrien impactar significativament en sectors com el cinema, la publicitat i els parcs temàtics, millorant les experiències dels usuaris de manera exponencial. El segon segment arroja llum sobre el problema cada vegada més crucial de la presa de decisions sota incertesa. Utilitzant el problema dels Multi-Armed Bandits (MAB) com a base, l'estudi explora l'ús dels LLMs per a informar i guiar estratègies en entorns dinàmics. Es postula que el poder predictiu dels LLMs pot ajudar a triar l'equilibri correcte entre exploració i explotació basat en l'estat actual del sistema. A través de proves rigoroses, l'estratègia informada pels LLMs proposada demostra la seua adaptabilitat i el seu rendiment competitiu front a les estratègies convencionals. A continuació, la recerca es centra en l'estudi de les avaluacions de bondat d'ajust de les Generative Adversarial Networks (GANs) utilitzant la Signature Transform. En proporcionar una mesura eficient de similitud entre les distribucions d'imatges, l'estudi arroja llum sobre l'estructura intrínseca de les mostres generades pels GANs. Una anàlisi exhaustiva utilitzant mesures estadístiques com les proves de Kruskal-Wallis proporciona una comprensió més àmplia de la convergència dels GANs i la bondat d'ajust. En la secció final, la tesi introdueix un nou benchmark per a la síntesi automàtica de vídeos, enfatitzant la integració harmònica dels LLMs i la Signature Transform. Es proposa un enfocament innovador basat en els components harmònics capturats per la Signature Transform. Les mesures són avaluades extensivament, demostrant oferir una precisió convincent que es correlaciona bé amb el concepte humà d'un bon resum. Aquest treball de recerca estableix els LLMs com a eines poderoses per a abordar tasques complexes en diversos dominis, redefinint l'optimització de decisions, la comprensió d'escenes i les tasques de resum de vídeo. No solament estableix nous postulats en les aplicacions dels LLMs, sinó que també estableix la direcció per a futurs treballs en aquest emocionant i ràpidament evolucionant camp. / [EN] The advent of Large Language Models (LLMs) marks a transformative phase in the field of Artificial Intelligence (AI), signifying the shift towards intelligent and autonomous systems capable of complex understanding and decision-making. This thesis delves deep into the multifaceted capabilities of LLMs, exploring their potential applications in decision optimization, scene understanding, and advanced summarization tasks in diverse contexts. In the first segment of the thesis, the focus is on Unmanned Aerial Vehicles' (UAVs) semantic scene understanding. The capability of instantaneously providing high-level data and visual cues positions UAVs as ideal platforms for performing complex tasks. The work combines the potential of LLMs, Visual Language Models (VLMs), and state-of-the-art detection pipelines to offer nuanced and contextually accurate scene descriptions. A well-controlled, efficient practical implementation of microdrones in challenging settings is presented, supplementing the study with proposed standardized readability metrics to gauge the quality of LLM-enhanced descriptions. This could significantly impact sectors such as film, advertising, and theme parks, enhancing user experiences manifold. The second segment brings to light the increasingly crucial problem of decision-making under uncertainty. Using the Multi-Armed Bandit (MAB) problem as a foundation, the study explores the use of LLMs to inform and guide strategies in dynamic environments. It is postulated that the predictive power of LLMs can aid in choosing the correct balance between exploration and exploitation based on the current state of the system. Through rigorous testing, the proposed LLM-informed strategy showcases its adaptability and its competitive performance against conventional strategies. Next, the research transitions into studying the goodness-of-fit assessments of Generative Adversarial Networks (GANs) utilizing the Signature Transform. By providing an efficient measure of similarity between image distributions, the study sheds light on the intrinsic structure of the samples generated by GANs. A comprehensive analysis using statistical measures, such as the test Kruskal-Wallis, provides a more extensive understanding of the GAN convergence and goodness of fit. In the final section, the thesis introduces a novel benchmark for automatic video summarization, emphasizing the harmonious integration of LLMs and Signature Transform. An innovative approach grounded in the harmonic components captured by the Signature Transform is put forth. The measures are extensively evaluated, proving to offer compelling accuracy that correlates well with the concept of a good summary. This research work establishes LLMs as powerful tools in addressing complex tasks across diverse domains, redefining decision optimization, scene understanding, and summarization tasks. It not only breaks new ground in the applications of LLMs but also sets the direction for future work in this exciting and rapidly evolving field. / De Curtò I Díaz, J. (2023). Frontiers of Large Language Models: Empowering Decision Optimization, Scene Understanding, and Summarization Through Advanced Computational Approaches [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202200 / Compendio

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting actions describe the same process at a coarse level, highlighting interactions between different meeting participants and showing overall group intentions. A framework based on probabilistic graphical models such as dynamic Bayesian networks (DBNs) has been investigated for both tasks. Our first set of experiments is concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these lowlevel multimodal features to complex group behaviours proposing a multistreammodelling framework based on dynamic Bayesian networks. Later experiments are concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative approach based on a switching DBN for DA recognition in which segmentation and classification of DAs are carried out in parallel. This approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. The DBN based approach yielded significant improvements when applied both to the meeting action and the dialogue act recognition task. On both tasks, the DBN framework provided an effective factorisation of the state-space and a flexible infrastructure able to integrate a heterogeneous set of resources such as continuous and discrete multimodal features, and statistical language models. Although our experiments have been principally targeted on multiparty meetings; features, models, and methodologies developed in this thesis can be employed for a wide range of applications. Moreover both group meeting actions and DAs offer valuable insights about the current conversational context providing valuable cues and features for several related research areas such as speaker addressing and focus of attention modelling, automatic speech recognition and understanding, topic and decision detection.

The present research focuses on students' attitudes and preferences towards English language learning at Czech private language schools. The study approaches language learning from two perspectives, the English as a Foreign Language (EFL) paradigm and the English as a Lingua Franca (ELF) paradigm. The key concepts discussed are: mistakes, re-labelled modifications, learner goals, learner needs, language models, accuracy and language creativity (or 'languaging'). Key words: EFL, ELF, learner goals, language models, accuracy, language creativity, languaging

Coreference is an important and frequent concept in any form of discourse, and Coreference Resolution (CR) a widely used task in Natural Language Understanding (NLU). In this thesis, we implement and explore two recent models that include the concept of coreference in Recurrent Neural Network (RNN)-based Language Models (LM). Entity and reference decisions are modeled explicitly in these models using attention mechanisms. Both models learn to save the previously observed entities in a set and to decide if the next token created by the LM is a mention of one of the entities in the set, an entity that has not been observed yet, or not an entity. After a theoretical analysis where we compare the two LMs to each other and to a state of the art Coreference Resolution system, we perform an extensive quantitative and qualitative analysis. For this purpose, we train the two models and a classical RNN-LM as the baseline model on the OntoNotes 5.0 corpus with coreference annotation. While we do not reach the baseline in the perplexity metric, we show that the models’ relative performance on entity tokens has the potential to improve when including the explicit entity modeling. We show that the most challenging point in the systems is the decision if the next token is an entity token, while the decision which entity the next token refers to performs comparatively well. Our analysis in the context of a text generation task shows that a wide-spread error source for the mention creation process is the confusion of tokens that refer to related but different entities in the real world, presumably a result of the context-based word representations in the models. Our re-implementation of the DeepMind model by Yang et al. 2016 performs notably better than the re-implementation of the EntityNLM model by Ji et al. 2017 with a perplexity of 107 compared to a perplexity of 131.

Detta arbete undersöker hur språkmodellen BERT och en MaLSTM-arkitektur fungerar att för att identifiera parafraser ur 'Microsoft Paraphrase Research Corpus' (MPRC) om dessa tränats på automatiskt identifierade parafraser ur 'Paraphrase Database' (PPDB). Metoderna ställs mot varandra för att undersöka vilken som presterar bäst och metoden att träna på maskinklassificerad data för att användas på mänskligt klassificerad data utvärderas i förhållande till annan klassificering av samma dataset. Meningsparen som används för att träna modellerna hämtas från de högst rankade parafraserna ur PPDB och genom en genereringsmetod som skapar icke-parafraser ur samma dataset. I resultatet visar sig BERT vara kapabel till att identifiera en del parafraser ur MPRC, medan MaLSTM-arkitekturen inte klarade av detta trots förmåga att särskilja på parafraser och icke-parafraser under träning. Både BERT och MaLSTM presterade sämre på att identifiera parafraser ur MPRC än modeller som till exempel StructBERT, som tränat och utvärderats på samma dataset, presterar. Anledningar till att MaLSTM inte klarar av uppgiften diskuteras och främst lyfts att meningarna från icke-parafraserna ur träningsdatan är för olika varandra i förhållande till hur de ser ut i MPRC. Slutligen diskuteras vikten av att forska vidare på hur man kan använda sig av maskinframtagna parafraser inom parafraseringsrelaterad forskning.

Current dialogue systems typically consist of separate components, which are manu- ally engineered to a large part and need extensive annotation. End-to-end trainable sys- tems exist but produce lower-quality, unreliable outputs. The recent transformer-based pre-trained language models such as GPT-2 brought considerable progress to language modelling, but they rely on huge amounts of textual data, which are not available for common dialogue domains. Therefore, training these models runs a high risk of overfit- ting. To overcome these obstacles, we propose a novel end-to-end dialogue system called AuGPT. We add auxiliary training objectives to use training data more efficiently, and we use massive data augmentation via back-translation and pretraining on multiple datasets to increase data volume and diversity. We evaluate our system using automatic methods (corpus-based metrics, user simulation), human evaluation as part of the DSTC 9 shared task challenge (where our system placed 3rd out of 10), as well as extensive manual error analysis. Our method substantially outperforms the baseline on the MultiWOZ bench- mark and shows competitive results with state-of-the-art end-to-end dialogue systems. 1

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.