While recent years have seen a rise of research in knowledge graph enrichedpre-trained language models(PLM), few studies have tried to transfer the work to the biomedical domain. This thesis is a first attempt to pre-train a large-scalebiological knowledge enriched language model (KPLM). Under the frameworkof CoLAKE (T. Sun et al., 2020), a general-use KPLM in general field, this study is pre-trained on PubMed abstracts (a large scale medical text data) andBIKG (AstraZeneca’s biological knowledge graph). We firstly get abstracts from PubMed and their entity linking results. Following this is to connect the entities from abstracts to BIKG to form sub-graphs. Such sub-graphs and sentences from PubMed abstracts are then sent to model CoLAKE for pre-training. By training the model on three objectives (masking word nodes, masking entity nodes and masking relation nodes), this research aims to not only enhancing model’s capacity on modeling natural language but also infusing in-depth knowledge. Later the model is fine-tuned on name entity recognition (NER) and relation extraction tasks on three benchmark datasets (Chemprot (Kringelumet al., 2016), DrugProt (form Text mining drug-protein/gene interactions sharedtask) and DDI (Segura-Bedmar et al., 2013)). Empirical results show that the model outperform state-of-the-art models relation extraction task on DDI dataset, with F1 score of 91.2%. Also on Drugprot and chemprot, this model shows improvement over baseline - scibert model.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24259号 / 情博第803号 / 新制||情||135(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 田島 敬史, 教授 黒橋 禎夫, 特定准教授 LIN Donghui / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This master thesis explores the effectiveness of interpolating a larger generic speech recognition model with smaller domain-specific models to enable transcription of domain-specific conversations. The study uses a corpus within the financial domain collected from the web and processed by abstracting named entities such as financial instruments, numbers, as well as names of people and companies. By substituting each named entity with a tag representing the entity type in the domain-specific corpus, each named entity can be replaced during the hypothesis search by words added to the systems pronunciation dictionary. Thus making instruments and other domain-specific terms a matter of extension by configuration.  A proof-of-concept automatic speech recognition system with the ability to transcribe and extract named entities within the constantly changing domain of voice trading was created. The system achieved a 25.08 Word Error Rate and 0.9091 F1-score using stochastic and neural net based language models. The best configuration proved to be a combination of both stochastic and neural net based domain-specific models interpolated with a generic model. This shows that even though the models were trained using the same corpus, different models learned different aspects of the material. The study was deemed successful by the authors as the Word Error Rate was improved by model interpolation and all but one named entities were found in the test recordings by all configurations. By adjusting the amount of influence the domain-specific models had against the generic model, the results improved the transcription accuracy at the cost of named entity recognition, and vice versa. Ultimately, the choice of configuration depends on the business case and the importance of named entity recognition versus accurate transcriptions.

Scientific findings recorded in literature help continuously guide scientific advancements, but manual approaches to accessing that knowledge are insufficient due to the sheer quantity of information and data available. Although pre-trained language models are being explored for their utility as knowledge bases and structured data repositories, there is a lack of research for this application in the biomedical domain. Therefore, the aim in this project was to determine how Generative Pre-trained Transformer models pre-trained on articles in the biomedical domain can be used to make relevant information more accessible. Several models (BioGPT, BioGPT-Large, and BioMedLM) were evaluated on the task of extracting chemical-protein relations between entities directly from the models through prompting. Prompts were formulated as a natural language text or an ordered triple, and provided in different settings (few-shot, one-shot, or zero-shot). Model-predictions were evaluated quantitatively as a multiclass classification task using a macro-averaged F1-score. The result showed that out of the explored methods, the best performance for extracting chemical-protein relations from article-abstracts was obtained using a triple-based text prompt on the largest model, BioMedLM, in the few-shot setting, albeit with low improvements from the baseline (+0.019 F1). There was no clear pattern for which prompt setting was favourable in terms of task performance, however, the triple based prompt was generally more robust than the natural language formulation. The task performance of the two smaller models underperformed the random baseline (by at best -0.026 and -0.001 F1). The impact of the prompt method was minimal in the smallest model, and the one-shot setting was the least sensitive to the prompt formulation in all models. However, there were more pronounced differences between the prompt methods in the few-shot setting of the larger models (+0.021-0.038 F1). The results suggested that the method of prompting and the size of the model impact the knowledge eliciting performance of a language model. Admittedly, the models mostly underperformed the baseline and future work needs to look into how to adapt generative language models to solve this task. Future research could investigate what impact automatic prompt-design methods and larger in-domain models have on the model performance. / De vetenskapliga upptäckter som presenteras inom litteraturen vägleder kontinuerligt vetenskapliga framsteg. Manuella tillvägagångssätt för att ta del av den kunskapen är otillräckliga på grund av den enorma mängd information och data som finns tillgänglig. Även om för-tränade språkmodeller utforskas för sin brukbarhet som kunskapsbaser och strukturerade dataförråd så finns det en brist på forskning inom den biomedicinska domänen. Målet med detta projekt var att utreda hur Generative Pre-trained Transformer (GPT) modeller för-tränade på biomedicinska artiklar kan användas för att öka tillgängligheten av relevant information inom denna domän. Olika modeller (BioGPT, BioGPT-Large, och BioMedLM) utvärderas på uppgiften att extrahera relationsinformation mellan entiteter direkt ur modellen genom en textprompt. En prompt formuleras genom naturlig text och som en ordnad trippel, och används i olika demonstrationsmiljöer (few-shot, one-shot, zero-shot). Modellförutsägelser utvärderas kvantitativt som ett multi-klass klassifikationsproblem genom ett genomsnittligt F1 värde. Resultatet indikerade att kemikalie-protein relationer från vetenskapliga artikelsammanfattningar kan extraheras med en högre sannolikhet än slumpen med en trippelbaserad prompt genom den största modellen, BioMedLM, i few-shot-miljön, dock med små förbättringar från baslinjen (+0.019 F1). Resultatet visade inga tydliga mönster gällande vilken demonstrationsmiljö som var mest gynnsam, men den trippelbaserade formuleringen var generellt mer robust än formuleringen som följde naturligt språk. Uppgiftsprestandan på de två mindre modellerna underpresterade den slumpmässiga baslinjen (med som bäst -0.026 och -0.001 F1). Effekten av valet av promptmetod var minimal med den minsta modellen, och one-shot-miljön var minst känslig för olika formuleringar hos alla modeller. Dock fanns det mer markanta skillnader mellan promptmetoder i few-shot-miljön hos de större modellerna (+0.021-0.038 F1). Resultatet antydde att valet av promptmetod och storleken på modell påverkar modellens förmåga att extrahera information. De utvärderade modellerna underpresterade dock baslinjen och fortsatt efterforskning behöver se över hur generativa språkmodeller kan anpassas för att lösa denna uppgift. Framtida forskning kan även undersöka vilken effekt automatiska promptdesignmetoder och större domänmodeller har på modellprestanda.

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Det här examensarbetet utforskar hur vårdpersonal i Sverige upplever implementeringen av artificiell intelligens (AI) i arbetsmiljön. Studien tar särskild hänsyn till de tekniska och etiska utmaningarna som medföljer vid införandet av AI-teknologier. Genom att använda teoretiska ramverk som Unified Theory of Acceptance and Use of Technology (UTAUT) och Diffusion of Innovations (DOI), ger arbetet insikt i både de individuella och organisatoriska aspekterna av teknikadoption. Data för den här kvalitativa studien samlades in genom semistrukturerade intervjuer med vårdpersonal. Intervjuerna fokuserade på deltagarnas personliga upplevelser och uppfattningar om AI, dess användbarhet och de utmaningar de står inför. Tematisk analys användes för att identifiera och analysera återkommande teman i den insamlade datan, vilket möjliggjorde en djupare förståelse av positiva aspekter och potentiella risker med AI enligt vårdpersonalen. De teman som identifierades var användningen av AI, avlasta sjukvårdspersonalen, förtroende och ansvar samt etik och orosområden med AI. Resultaten visar på att medan AI erbjuder betydande potential för att förbättra effektiviteten och kvaliteten på både processer och patientvården, finns det dock en betydande oro för frågor som rör dataskydd, patientintegritet och den potentiella risken för jobbersättning. Studien belyser vikten av att utveckla klara riktlinjer och regelverk för att hantera dessa utmaningar på etiskt och korrekt vis.  Det här arbetet bidrar till debatten om AI:s vara och icke vara samt roll i sjukvården och understryker behovet av en välbalanserad och välinformerad approach till teknikintegration, vilket är avgörande för att säkerställa både innovationens fördelar och vårdtagarnas samt personalens välbefinnande. Den här undersökningen bidrar till ökad och fördjupad förståelse för den dynamiska roll AI har i sjukvården. / This thesis explores how healthcare staff in Sweden experience the implementation of artificial intelligence (AI) in their work environment. The study pays special attention to the technical and ethical challenges that accompany the introduction of AI technologies in healthcare. By using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and Diffusion of Innovations (DOI), the work provides insights into both the individual and organizational aspects of technology adoption. Data for the qualitative study were collected through semi-structured interviews with healthcare personnel from various offices. The interviews focused on the participants' experiences and their perceptions of AI, its usability, and the challenges they face. Thematic analysis was used to identify and analyse recurring themes in the collected data, enabling a deeper understanding of the positive aspect and potential risks of AI in healthcare. The themes that were identified included the use of AI, relieving healthcare personnel, trust, and responsibility, as well as ethics and concerns related to AI. The results show that while AI offers significant potential to improve both the efficiency and quality of process and patient care, there is considerable concern regarding the issues related to data protection, patient privacy, and the potential risk of job displacement. The study highlights the importance of developing clear guidelines and regulations to address these challenges in an ethical and correct manner.  This research contributes to the debate on the pros and cons and role of AI in healthcare and underscores the need for a well-balanced and well-informed approach to technology integration, which is crucial to ensuring both the benefits of innovation and the well-being of patients and staff. This investigation contributes to an increased and deeper understanding of the dynamic role AI has in healthcare.

<p dir="ltr">Task-oriented programming is a way of programming manipulators in terms of high-level tasks instead of explicit motions. It has been a long-standing vision in robotics since its early days. Despite its potential, several challenges have hindered its full realization. This thesis identifies three major challenges, particularly in task specification and the planning-to-execution transition: 1) The absence of natural language integration in system input. 2) The dilemma of continuously developing non-uniform and domain-specific primitive-task libraries. 3) The requirement for much human intervention.</p><p dir="ltr">To overcome these difficulties, this thesis introduces a novel approach that integrates natural language inputs, eliminates the need on fixed primitive-task libraries, and minimizes human intervention. It adopts the behavior tree, a modular and user-friendly form, as the task representation and advances its usage in task specification and planning-to-execution transition. The thesis is structured into two parts – Task Specification and Planning-to-Execution Transition.</p><p dir="ltr">Task specification explores the use of large language models to generate a behavior tree from an end-user's input. A Phase-Step prompt is designed to enable the automatic behavior-tree generation from end-user's abstract task descriptions in natural languages. With the powerful generalizability of large language models, it breaks the dilemma that stays with fixed primitive-task libraries in task generation. A full-process case study demonstrated the proposed approach. An ablation study was conducted to evaluate the effectiveness of the Phase-Step prompts. Task specification also proposes behavior-tree embeddings to facilitate the retrieval-augmented generation of behavior trees. The integration of behavior-tree embeddings not only eliminates the need for manual prompt configuration but also provides a way to incorporate external domain knowledge into the generation process. Three types of evaluations were performed to assess the performance of the behavior-tree embedding method.</p><p dir="ltr">Planning-to-execution transition explores how to transit primitive tasks from task specification into manipulator executions. Two types of primitive tasks are considered separately: point-to-point movement tasks and object-interaction tasks. For point-to-point movement tasks, a behavior-tree reward is proposed to enable reinforcement learning over low-level movement while following high-level running order of the behavior tree. End-users only need to specify rewards on the primitive tasks over the behavior tree, and the rest of the process will be handled automatically. A 2D space movement simulation was provided to justify the approach. For object-interaction tasks, the planning-to-execution transition uses a large-language-model-based generation approach. This approach takes natural-language-described primitive tasks as input and directly produces task-frame-formalism set-points. Combined with hybrid position/force control systems, a transition process from primitive tasks directly into joint-level execution can be realized. Evaluations over a set of 30 primitive tasks were conducted.</p><p dir="ltr">Overall, this thesis proposes an approach that advances the behavior-tree towards automated task specification and planning-to-execution transitions. It opens up new possibilities for building better task-oriented manipulator programming systems.</p>

[pt] A tarefa chamada brevemente de Texto-para-SQL envolve a geração de uma consulta SQL com base em um banco de dados relacional e uma pergunta em linguagem natural. Embora os rankings de benchmarks conhecidos indiquem que Modelos de Linguagem Grandes (LLMs) se destacam nessa tarefa, eles são avaliados em bancos de dados com esquemas bastante simples. Esta dissertação investiga inicialmente o desempenho de modelos Texto-para-SQL baseados em LLMs em um banco de dados disponível ao público (Mondial)com um esquema conceitual complexo e um conjunto de 100 perguntas em Linguagem Natural (NL). Executando sob GPT-3.5 e GPT-4, os resultados deste primeiro experimento mostram que as ferramentas baseadas em LLM têm desempenho significativamente inferior ao relatado nesses benchmarks e enfrentam dificuldades com a vinculação de esquemas e joins, sugerindo que o esquema relacional pode não ser adequado para LLMs. Essa dissertação propõe então o uso de visões e descrições de dados amigáveis ao LLM para melhorara precisão na tarefa Texto-para-SQL. Em um segundo experimento, usando a estratégia com melhor performance, custo e benefício do experimento anterior e outro conjunto com 100 perguntas sobre um banco de dados do mundo real, os resultados mostram que a abordagem proposta é suficiente para melhorar consideravelmente a precisão da estratégia de prompt. Esse trabalho conclui com uma discussão dos resultados obtidos e sugere abordagens adicionais para simplificar a tarefa de Texto-para-SQL. / [en] The Text-to-SQL task involves generating an SQL query based on a given relational database and a Natural Language (NL) question. While the leaderboards of well-known benchmarks indicate that Large Language Models (LLMs) excel in this task, they are evaluated on databases with simpler schemas. This dissertation first investigates the performance of LLM-based Text-to-SQL models on a complex and openly available database (Mondial) with a large schema and a set of 100 NL questions. Running under GPT-3.5 and GPT-4, the results of this first experiment show that the performance of LLM-based tools is significantly less than that reported in the benchmarks and that these tools struggle with schema linking and joins, suggesting that the relational schema may not be suitable for LLMs. This dissertation then proposes using LLM-friendly views and data descriptions for better accuracy in the Text-to-SQL task. In a second experiment, using the strategy with better performance, cost and benefit from the previous experiment and another set with 100 questions over a real-world database, the results show that the proposed approach is sufficient to considerably improve the accuracy of the prompt strategy. This work concludes with a discussion of the results obtained and suggests further approaches to simplify the Text-to-SQL task.

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

[pt] Os documentos são essenciais para o sistema econômico e acadêmico; no entanto, explorá-los pode ser uma tarefa complexa e demorada. Uma abordagem para contornar esse problema é o uso de modelos de Visual Question and Answering (VQA) para extração de informações de documentos por meio de prompts em linguagem natural. No VQA, assim como para o desenvolvimento dos mais variados modelos, é necessário possuir dados anotados para a sua etapa de treinamento e validação. No entanto, criar esses conjuntos de dados é desafiador devido ao alto custo envolvido no processo. Com base nisso, propomos um processo de quatro etapas que combina Modelos de Visão Computacional e Large Language Models (LLMs) para a anotação de dados de VQA em relatórios financeiros. O método proposto inicia pelo reconhecimento da estrutura textual dos documentos por meio de modelos de Análise de Layout de Documentos e Extração de Estrutura de Tabelas. Em seguida, utiliza duas LLMs distintas para a etapa de geração e avaliação dos pares de perguntas e respostas geradas, automatizando a construção e seleção dos melhores pares para compor a base final. Para avaliar o método proposto, geramos um dataset para treinar e avaliar modelos especialistas em VQA. / [en] Documents are essential for the economic and academic system; however, exploring them can be complex and time-consuming. An approach to surpass this problem is the use of Visual Question and Answering (VQA) models to extract information from documents through natural language prompts. In VQA, as well as for the development of various models, it is necessary to have annotated data for training and validation. However, creating these datasets is challenging due to the high cost involved in the process. To face this challenge, we propose a four-step process that combines Computer Vision Models and Large Language Models (LLMs) for VQA data annotation in financial reports. The proposed method starts with recognizing the textual structure of documents through Document Layout Analysis and Table Structure Extraction models. Then, it uses two distinct LLMs for the generation and evaluation of question and answer pairs, automating the construction and selection of the best pairs to compose the final dataset. To evaluate the proposed method, we generate a dataset for train and evaluate VQA specialized models.