Natural Language Dialogue Research Articles

UTILIZING RETRIEVAL-AUGMENTED GENERATION IN LARGE LANGUAGE MODELS TO ENHANCE INDONESIAN LANGUAGE NLP

The improvement of Large Language Models (LLM) such as ChatGPT through Retrieval-Augmented Generation (RAG) techniques has urgency in the development of natural language translation technology and dialogue systems. LLMs often experience obstacles in addressing special requests that require information outside the training data. This study aims to discuss the use of Retrieval-Augmented Generation (RAG) on large-scale language models to improve the performance of Natural Language Processing (NLP) in Indonesian, which has so far been poorly supported by high-quality data and to overcome the limitations of traditional language models in understanding the context of Indonesian better. The method used is a combination of retrieval capabilities (external information search) with generation (text generation), where the model utilizes broader and more structured basic data through the retrieval process to produce more accurate and relevant text. The data used includes the Indonesian corpus of the 30 Juz Quran translation into Indonesian. The results of the trial show that the RAG approach significantly improves the performance of the model in various NLP tasks, including token usage optimization, text classification, and context understanding, by increasing the accuracy and relevance of the results

Customizing GPT for natural language dialogue interface in database access

The paper presents Anatomy3DExplorer, a customized ChatGPT designed as a natural language dialogue interface for exploring 3D models of anatomical structures. It illustrates the significant potential of large language models (LLMs) as user-friendly interfaces for database access. Furthermore, it showcases the seamless integration of LLMs and database APIs, within the GPTS framework, offering a promising and straightforward approach.

Reinforcement Learning in Social Sciences: A Survey

Reinforcement Learning (RL) has become one of the most prominent topics in artificial intelligence research. It is widely used in various fields, such as recommendation systems, psychology, economics, and natural language dialogue systems. Finding the best path of action to maximize cumulative reward is the long-term strategy of RL. Undertaking research may yield suboptimal immediate results but optimal long-term consequences. Economists can address difficult behavioral problems with knowledge, especially those generated by deep learning algorithms. We provide the most recent advancements in RL methods in this study, along with their applications in gaming, finance, and economics. The survey's last section discusses RL's present problems and potential future developments. Such open problems as sample efficiency, safety, and interpretability are currently being sought after by researchers. Moreover, several ambitious prospective applications of RL in a wide variety of domains are discussed. This study gives a comprehensive review of the many methods and uses of RL in social science. This study's results will give researchers a standard against which to evaluate the utility and efficacy of frequently used RL. Guide future investigations across several domains.

Privacy Preservation of Large Language Models in the Metaverse Era: Research Frontiers, Categorical Comparisons, and Future Directions

ABSTRACTLarge language models (LLMs), with their billions to trillions of parameters, excel in natural language processing, machine translation, dialog systems, and text summarization. These capabilities are increasingly pivotal in the metaverse, where they can enhance virtual interactions and environments. However, their extensive use, particularly in the metaverse's immersive platforms, raises significant privacy concerns. This paper analyzes existing privacy issues in LLMs, vital for both traditional and metaverse applications, and examines protection techniques across the entire life cycle of these models, from training to user deployment. We delve into cryptography, embedding layer encoding, differential privacy and its variants, and adversarial networks, highlighting their relevance in the metaverse context. Specifically, we explore technologies like homomorphic encryption and secure multiparty computation, which are essential for metaverse security. Our discussion on Gaussian differential privacy, Renyi differential privacy, Edgeworth accounting, and the generation of adversarial samples and loss functions emphasizes their importance in the metaverse's dynamic and interactive environments. Lastly, the paper discusses the current research status and future challenges in the security of LLMs within and beyond the metaverse, emphasizing urgent problems and potential areas for exploration.

Exploring the Potential of Large Language Models in Radiological Imaging Systems: Improving User Interface Design and Functional Capabilities

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including conversation, in-context learning, reasoning, and code generation. This paper explores the potential application of LLMs in radiological information systems (RIS) and assesses the impact of integrating LLMs on RIS development and human–computer interaction. We present ChatUI-RIS, a prototype chat-based user interface that leverages LLM capabilities to enhance RIS functionality and user experience. Through an exploratory study involving 26 medical students, we investigate the efficacy of natural language dialogue for learning and operating RIS. Our findings suggest that LLM integration via a chat interface can significantly improve operational efficiency, reduce learning time, and facilitate rapid expansion of RIS capabilities. By interacting with ChatUI-RIS using natural language instructions, medical students can access and retrieve radiology information in a conversational manner. The LLM-powered chat interface not only streamlines user interactions, but also enables more intuitive and efficient navigation of complex RIS functionalities. Furthermore, the natural language processing capabilities of LLMs can be harnessed to automatically generate code snippets and database queries, accelerating RIS development and customization. Preliminary observations indicate that integrating LLMs in RIS has the potential to revolutionize user interface design, enhance system capabilities, and ultimately improve the overall user experience for radiologists and medical professionals.

How Can Large Language Models Enable Better Socially Assistive Human-Robot Interaction: A Brief Survey

Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental health challenges. The large body of work on SAR demonstrates its potential to provide at-home support that complements clinic-based interventions delivered by mental health professionals, making these interventions more effective and accessible. However, there are still several major technical challenges that hinder SAR-mediated interactions and interventions from reaching human-level social intelligence and efficacy. With the recent advances in large language models (LLMs), there is an increased potential for novel applications within the field of SAR that can significantly expand the current capabilities of SARs. However, incorporating LLMs introduces new risks and ethical concerns that have not yet been encountered, and must be carefully be addressed to safely deploy these more advanced systems. In this work, we aim to conduct a brief survey on the use of LLMs in SAR technologies, and discuss the potentials and risks of applying LLMs to the following three major technical challenges of SAR: 1) natural language dialog; 2) multimodal understanding; 3) LLMs as robot policies.

YouTube Scholar: Advancing Education with AI Assistance

In the contemporary landscape of online educa- tion, the proliferation of educational content on platforms like YouTube has opened new avenues for learners. However, the sheer volume of videos presents a challenge, requiring innovative solutions. This project takes a pioneering approach by developing a specialized AI assistant for educational YouTube lectures, lever- aging advanced language models such as Generative Pretrained Transformers (GPT). This project focuses on the development of an AI assistant tailored specifically for educational YouTube lectures and courses. The primary goal is to enhance the learning experience by leveraging the capabilities of GPT-based models to provide personalised and contextually relevant interactions for learners. By harnessing the power of natural language understanding, generation, and dialog systems, the AI assistant aims to assist learners in navigating, understanding, and engaging with educa- tional content more effectively. The ultimate goal is to bridge the gap between the abundance of online educational content and learners’ comprehension, thereby revolutionizing the educational landscape in the digital age. By providing a sophisticated AI-driven companion, this project aspires to empower learners, making the educational journey more accessible, engaging, and conducive to deeper learning experiences. Through the integration of cutting-edge technologies, it envisions a future where learners can unlock the full potential of online education with confidence and ease

HOMMIE: CONVERSATIONAL AI ASSISTANT

Conversational AI assistants have witnessed remarkable progress in recent years, transforming human-computer interaction across a spectrum of applications. This paper offers a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field. We examine the core components of conversational AI, including natural language understanding, dialogue management, and response generation. Additionally, we address key challenges such as context modeling, personalization, and ethical considerations. The paper serves as a roadmap for researchers and developers, highlighting current achievements and avenues for future advancements in the dynamic domain of conversational AI. keyword - Conversational AI, NLP, Assistant, Machine Learning, Chatbot

TrafficGPT: Viewing, processing and interacting with traffic foundation models

With the promotion of ChatGPT to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, large language models (LLMs) struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT—a fusion of multiple LLMs and traffic foundation models. This integration yields the following key enhancements: 1) empowering LLMs with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git.

MedChatZH: A tuning LLM for traditional Chinese medicine consultations

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs.

A flood knowledge-constrained large language model interactable with GIS: enhancing public risk perception of floods

Public’s rational flood mitigation behaviors depend on accurate perception of flood risks. The use of natural language for flood risk perception is an effective approach, and it is critical to ensure the accuracy and comprehensibility of the flood information provided by the system in natural language dialogues. This study presents a framework for large language model (LLM) that is constrained by flood knowledge and can interact with geographic information system (GIS), aimed at enhancing the public’s perception of flood risks. We tested the performance of LLM within this framework and the results demonstrate that LLM can generate accurate information about floods under the constraints of entities and relationships in the knowledge graph, and interact with GIS to produce personalized knowledge through real-time coding. Furthermore, we conducted flood risk perception experiments on users with different cognitive levels. The results indicate that using natural language dialogue can narrow the differences brought about by cognitive levels, allowing the public to equally access knowledge related to flood events.

Incremental learning of humanoid robot behavior from natural interaction and large language models.

Natural-language dialog is key for an intuitive human-robot interaction. It can be used not only to express humans' intents but also to communicate instructions for improvement if a robot does not understand a command correctly. It is of great importance to let robots learn from such interaction experiences in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve such incremental learning of complex high-level behavior from natural interaction and demonstrate its implementation on a humanoid robot. Our system deploys large language models (LLMs) for high-level orchestration of the robot's behavior based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. Human instructions, environment observations, and execution results are fed back to the LLM, thus informing the generation of the next statement. Since an LLM can misunderstand (potentially ambiguous) user instructions, we introduce incremental learning from the interaction, which enables the system to learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements in the current interaction based on human feedback. Subsequently, we store the improved interaction in the robot's memory so that it can later be retrieved on semantically similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally learned knowledge.

A new design approach of hardware implementation through natural language entry

AbstractOpenAI's ChatGPT (GPT‐4) ushers in a superior mode of computer interaction through natural language dialogues. Notably, it generates not only engaging dialogues but also codes aligned to queries and requirements. The potential of ChatGPT in hardware implementation via natural language is implemented and a strategy for “asking the right questions” is outlined. The versatility of ChatGPT is demonstrated through three mainstream hardware designs – systolic array, ResNet and MobileNet accelerators – comparing these with hand‐coded designs. The evaluation metrics include design quality, design efforts, and limitations of code generated by ChatGPT/GPT‐4/Cursor against prevalent High‐Level Synthesis or hand‐coded HDL designs. Consequently, a novel design workflow is proposed and the constraints of using GPT, particularly in AI accelerators, are highlighted.

Intelligent Educational Recommendation Platform with AI Chatbots

The objectives of this research were as follows. 1) Analyze the intelligent educational recommendation platform with AI Chatbots. 2) Design the architecture of the intelligent educational recommendation platform with AI Chatbots. 3) Develop the architecture of the intelligent educational recommendation platform with AI Chatbots. 4) Study the appropriateness of developing the intelligent educational recommendation platform with AI Chatbots. The sample used in the research was seven experts in information system development from various institutions in higher education. The architecture of the intelligent educational recommendation platform with AI Chatbots there is two main components: 1) Stakeholders consisting of system administrators and external users, and 2) The working process of the intelligent educational recommendation platform with AI Chatbots consists of four parts including natural language processing, dialog management, database and application programming interface (API), and response generation. Assessment of the appropriateness of the architecture of the intelligent educational recommendation platform with AI Chatbots found that 1) the architecture of the intelligent educational recommendation platform with AI Chatbots, overall at a high appropriated, 2) the architecture of the intelligent educational recommendation platform with AI Chatbots, an individual element at a high appropriated, and 3) the architecture of the intelligent educational recommendation platform with AI Chatbots, Integrated elements at a high appropriated. As described earlier, the architecture of the intelligent educational recommendation platform with AI Chatbots can be a guideline for developing with AI Chatbots in the future.

Ontology-driven development of dialogue systems

A new technique and its software implementation are presented to create a deeply semantically structured ontology using plain natural language text as input, without regular structure or any previous tagging and markup. The new approach is primarily aimed at highly inflectional languages, and is implemented for Ukrainian. The automatically created ontologies (in OWL) could be easily converted to other graph databases formats, such as Neo4j, and were successfully evaluated as valid ontologies using Protégé, RDFlib and Neo4j environments. An integrated approach is proposed for the development of natural language dialogue systems driven by the ontologyrelated graph database using the Cypher language for the formal queries. The original phrases are subject to a special method of semantic analysis, which determines the type of formal query to the database. The essence of the analysis is that the text of the user’s phrase goes through a series of checks. Based on their results, a set of basic templates for the formal requests are determined, as well as additional constructions that are attached to the basic template. Some of the checks may also return the notion of substitution to certain specified positions of the formal query. Formal queries can return both contexts and lists of ontology concepts. In addition to concepts, queries can also return information about specific semantic predicates that connect them, which simplifies the synthesis of natural language responses. The synthesis of answers is based on special templates, the choice of which is directly related to the corresponding template of the formal query.

Exploring the Application Potential of the Large Language Model in Sociological Research: A Case Study of ChatGPT

With the exponential growth of Big Data and Artificial Intelligence (AI), large Language models have emerged as powerful tools in sociological research. As an advanced large Language model, ChatGPT has the unique ability to generate natural language dialogues, opening up new avenues for sociological research. This paper employs ChatGPT as a case study to explore the application potential of large Language models in sociological research, including social network analysis, sentiment analysis, opinion mining and social public opinion monitoring. Additionally, we discuss the various challenges encountered by large Language models in sociological research, such as privacy, ethical, interpretability and credibility issues. Finally, some recommendations to overcome these challenges and prospective research directions are provided.

Enhancing the conversational agent with an emotional support system for mental health digital therapeutics.

As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines.

RV4JaCa—Towards Runtime Verification of Multi-Agent Systems and Robotic Applications

This paper presents a Runtime Verification (RV) approach for Multi-Agent Systems (MAS) using the JaCaMo framework. Our objective is to bring a layer of security to the MAS. This is achieved keeping in mind possible safety-critical uses of the MAS, such as robotic applications. This layer is capable of controlling events during the execution of the system without needing a specific implementation in the behaviour of each agent to recognise the events. In this paper, we mainly focus on MAS when used in the context of hybrid intelligence. This use requires communication between software agents and human beings. In some cases, communication takes place via natural language dialogues. However, this kind of communication brings us to a concern related to controlling the flow of dialogue so that agents can prevent any change in the topic of discussion that could impair their reasoning. The latter may be a problem and undermine the development of the software agents. In this paper, we tackle this problem by proposing and demonstrating the implementation of a framework that aims to control the dialogue flow in a MAS; especially when the MAS communicates with the user through natural language to aid decision-making in a hospital bed allocation scenario.

Multi-grained hypergraph interest modeling for conversational recommendation

Conversational recommender system (CRS) interacts with users through multi-turn dialogues in natural language, which aims to provide high-quality recommendations for user’s instant information need. Although great efforts have been made to develop effective CRS, most of them still focus on the contextual information from the current dialogue, usually suffering from the data scarcity issue. Therefore, we consider leveraging historical dialogue data to enrich the limited contexts of the current dialogue session.In this paper, we propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data from different perspectives. As the core idea, we employ hypergraph to represent complicated semantic relations underlying historical dialogues. In our approach, we first employ the hypergraph structure to model users’ historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations. Second, to alleviate the issue of data scarcity, we use an external knowledge graph and construct a knowledge-based hypergraph considering fine-grained, entity-level semantics. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS. Extensive experiments on two benchmarks ReDial and TG-ReDial validate the effectiveness of our approach on both recommendation and conversation tasks. Code is available at: https://github.com/RUCAIBox/MHIM.

ASSISTENTE VIRTUAL: FERRAMENTA PARA COPRODUÇÃO EM SAÚDE NO ENFRENTAMENTO À COVID-19

ABSTRACT Objective: to describe the development of a virtual assistant as a potential tool for health co-production in coping with COVID-19. Method: this is an applied technological production research study developed in March and April 2020 in five stages: 1) literature review, 2) content definition, 3) elaboration of the dialog, 4) test of the prototype, and 5) integration with the social media page. Results: the literature review gathered diverse scientific evidence about the disease based on the Brazilian Ministry of Health publications and by consulting scientific articles. The content was built from the questions most asked by the population, in March 2020, evidenced by Google Trends, in which the following topics emerged: concept of the disease, prevention means, transmission of the disease, main symptoms, treatment modalities, and doubts. Elaboration of the dialog was based on Natural Language Processing, intentions, entities and dialog structure. The prototype was tested in a laboratory with a small number of user computers on a local network to verify the functionality of the set of apps, technical and visual errors in the dialog, and whether the answers provided were in accordance with the user's question, answering the questions correctly and integrated into Facebook. Conclusion: the virtual assistant proved to be a health education tool with potential to combat “Fake News”. It also represents a patient-centered form of health communication that favors the strengthening of the bond and interaction between health professionals and patients, promoting co-production in health.