The development of natural language processing algorithms and the explosive growth of conversational data are encouraging researches on the human-computer conversation. Still, getting qualified conversational data on a large scale is difficult and expensive. In this paper, we verify the feasibility of constructing a data-driven chatbot with processed online community posts by using them as pseudo-conversational data. We argue that chatbots for various purposes can be built extensively through the pipeline exploiting the common structure of community posts. Our experiment demonstrates that chatbots created along the pipeline can yield the proper responses.
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model. We also develop two evaluation metrics to estimate if the respo…
Can AI Chatbots Pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) Structural Exams?
Clemson University（克莱姆森大学）；Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University（科学与工程人工智能研究所（AIRISE），克莱姆森大学）；Michigan State University（密歇根州立大学）；American University of Sharjah（沙迦美国大学）；The University of Melbourne（墨尔本大学）
本研究评估了 ChatGPT-4 和 Bard 在 FE 和 PE 工程考试中的表现，发现 ChatGPT-4 有望通过 FE 考试，并探讨了其作为助教和指导工程师的潜力。
Columbia University（哥伦比亚大学）；University of California, Davis（加州大学戴维斯分校）；Zhejiang University（浙江大学）
This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice partners. Our system can adapt to users' language proficiency on the fly. We also provide automatic grammar error feedback to help users learn from their mistakes. According to our first adopters, our system is entertaining and useful. Furthermore, we will provide the learning technology community a large-scale conversation dataset on language learning and grammar correction. Our next step is to make our system more…
History of generative Artificial Intelligence (AI) chatbots: past, present, and future development
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Asian University of Bangladesh（亚洲孟加拉大学）；Sonargaon University（索纳尔冈大学）；Babes-Bolyai University（巴比什-博雅依大学）；Islamic University（伊斯兰大学）；University of Chittagong（吉大港大学）
Companies are dealing with many cognitive changes with the introduction of the Industry 4.0 paradigm. In this constantly changing environment, knowledge management is a key factor. Dialog systems, being able to hold a conversation with humans, could support the knowledge management in business environment. Although, these systems are currently hand-coded and need the intervention of a human being in writing all the possible questions and answers, and then planning the interactions. This process, besides being time-consuming, is not scalable. Conversely, a dialog system, also referred to as cha…
Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes
National University of Singapore（国立新加坡大学）；Yunnan University（云南大学）；The Ohio State University（俄亥俄州立大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
