The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity

This demo paper presents JayBot, an LLM-based chatbot system aimed at enhancing the user experience of prospective and current students, faculty, and staff at a UK university. The objective of JayBot is to provide information to users on general enquiries regarding course modules, duration, fees, entry requirements, lecturers, internship, career paths, course employability and other related aspects. Leveraging the use cases of generative artificial intelligence (AI), the chatbot application was built using OpenAI’s advanced large language model (GPT-3.5 turbo); to tackle issues such as hallucination as well as focus and timeliness of results, an embedding transformer model has been combined with a vector database and vector search. Prompt engineering techniques were employed to enhance the chatbot’s response abilities. Preliminary user studies indicate JayBot’s effectiveness and efficiency. The demo will showcase JayBot in a university admission use case and discuss further application scenarios.

Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.