Pesquisa de Doutorado da Veronica

https://knowledge-nlp.github.io/kdd2023/papers/Kumar5.pdf https://github.com/isunitha98selvan/odqa-tail ABSTRACT Pretrained Large Language Models (LLMs) have gained significant attention for addressing open-domain Question Answering (QA). While they exhibit high accuracy in answering questions related to common knowledge, LLMs encounter difficulties in learning about uncommon long-tail knowledge (tail entities).   [Entidades com poucas informações disponíveis, não tão populares ou comuns no interesse do público em geral] 1 INTRODUCTION However, the impressive achievements of LLMs in QA tasks are primarily observed with regard to common concepts that frequently appear on the internet (referred to as "head entities"), which are thus more likely to be learned effectively by LLMs during pretraining time. Conversely, when it comes to dealing with long-tail knowledge, which encompasses rarely occurring entities (referred to as "tail entities"), LLMs struggle to provide ac...

Enhancing recommendations with contrastive learning from collaborative knowledge graph https://doi.org/10.1016/j.neucom.2022.12.032 Abstract There have been excellent results using knowledge graphs in recommender systems. Knowledge graphs can be used as auxiliary information to alleviate data sparsity and strengthen the modeling of item sets and the representation of user preferences. However, users as the Core subject in the recommendation process, should be taken seriously. We believe that the user's choice of items will be affected by internal and external factors. Internal factors refer to the users’ fuzzy interest sets, which initially affect the users' choices. External factors refer to the influence of similar users and similar items in the users' selection of items. [Os itens recomendados ainda passam por um processo de decisão do usuário] Introduction There is a lot of knowledge on today's Internet. Generally speaking, this knowledge is not isolated but interre...