Related papers: ChatGPT versus Traditional Question Answering for …
While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been…
In the current era, a multitude of language models has emerged to cater to user inquiries. Notably, the GPT-3.5 Turbo language model has gained substantial attention as the underlying technology for ChatGPT. Leveraging extensive parameters,…
Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in enterprise and domain applications to provide structured,…
In this paper, we present work in progress on the role of artificial intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to federated knowledge graphs. In particular, we provide examples from the field of…
Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines. KGs…
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational…
Knowledge graphs (KGs) are essential in applications such as network alignment, question-answering, and recommender systems (RSs) since they offer structured relational data that facilitate the inference of indirect relationships. However,…
In the rapidly evolving domain of artificial intelligence, chatbots have emerged as a potent tool for various applications ranging from e-commerce to healthcare. This research delves into the intricacies of chatbot technology, from its…
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun…
Conversational generative AI systems such as ChatGPT are transforming how people seek and engage with information online. Unlike traditional search engines, these systems support open-ended, conversational inquiry, yet it remains unclear…
Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to encode the Knowledge Graph (KG) information. However, most…
In recent years, Natural Language Processing (NLP) has played a significant role in various Artificial Intelligence (AI) applications such as chatbots, text generation, and language translation. The emergence of large language models (LLMs)…
Large Language Models (LLMs) and Knowledge Graphs (KGs) offer a promising approach to robust and explainable Question Answering (QA). While LLMs excel at natural language understanding, they suffer from knowledge gaps and hallucinations.…
Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face significant challenges, including high…
Generative AI is changing the way in which humans seek to find answers to questions in different fields including on the gig economy and labour markets, but there is limited information available about closely ChatGPT simulated output…
ChatGPT is a natural language processing tool that can engage in human-like conversations and generate coherent and contextually relevant responses to various prompts. ChatGPT is capable of understanding natural text that is input by a user…
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have…
Previous studies have relied on existing question-answering benchmarks to evaluate the knowledge stored in large language models (LLMs). However, this approach has limitations regarding factual knowledge coverage, as it mostly focuses on…
Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific…
In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information. This makes them attractive for systems that supplement or ground the knowledge found in pre-trained language…