Pesquisa de Doutorado da Veronica

Hogan, A., Dong, X.L., Vrandevci'c, D., & Weikum, G. (2025). Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions. A BACKGROUND A.1 Large Language Models LLMs capture contextual probabilities of tokens in the parameters of a large neural network, often following the Transformer architecture [44]. The model parameters are computed by two stages of training: unsupervised pre-training and supervised fine-tuning . LLMs can also benefit from inference-time (i.e., post-training) techniques, most notably, prompt engineering [26] and in-context learning [9]. The usual training objective is to predict the next token in a text sequence, repeatedly in an auto-regressive manner. As the original text is available, the ground-truth is known and this entire training process is completely unsupervised (or self-supervised, as it is sometimes phrased). Fine-tuning adopts a pre-trained LLM as a foundational model and adapts it for a sui...

Hogan, A., Dong, X.L., Vrandevci'c, D., & Weikum, G. (2025). Large Language Models, Knowledge Graphs and Search Engines: A Crossroads for Answering Users' Questions. 3 THE PERSPECTIVE OF INFORMATION-SEEKING USERS Todas as categorias podem estar presentes em processos de tomada de decisão Facts: Users seek objective, verifiable information that may be satisfied by a simple answer: a name, number, list, table, etc. Algumas variam com o tempo, outras exploram várias arestas (caminhos). Explanations: Users seek an explanation as to what something is, what caused it, what properties it has, how it works, etc., based principally on objective criteria. Exploratory queries involve gaining initial understanding into an unfamiliar topic, or further understanding into a familiar one. In this case, the user may not know precisely what they are looking for, but will rather hope to recognize it when they see it. Perguntas incompletas. Planning: Users seek information to help them tak...