Titlebook: Natural Language Processing and Information Systems; 29th International C Amon Rapp,Luigi Di Caro,Vijayan Sugumaran Conference proceedings

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,Generating Entity Embeddings for?Populating Wikipedia Knowledge Graph by?Notability Detection,ulating KGs, these methods typically do not focus on analyzing entity-specific content exclusively?but rely on a fixed collection of documents. We define an approach?to populate such KGs by utilizing entity-specific content on the web, for generating entity embeddings. We empirically prove?our appro

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,Think from?Words(TFW): Initiating Human-Like Cognition in?Large Language Models Through Think from?(IL), In-context Learning (ICL), and Chain-of-Thought (CoT). These approaches aim to improve LLMs’ responses by enabling them to provide concise statements or examples for deeper contemplation when addressing questions. However, independent thinking by LLMs can introduce variability in their thought

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,Token Trails: Navigating Contextual Depths in?Conversational AI with?ChatLLM,responses. In this paper, we present ., a novel approach that leverages Token-Type Embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes Token-Type Embeddings to distinguish between user utterances and bot responses, facilitating the generation of conte

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,: A Strong Baseline for?Simple Knowledge Graph Question Answering,risingly, even most powerful modern Large Language Models (LLMs) are prone to errors when dealing?with such questions, especially when dealing with rare entities. At?the same time, as an answer may be one hop away from the question entity, one can try to develop a method that uses structured knowled