Riccardo Pozzi

Riccardo Pozzi, Matteo Palmonari, Andrea Coletta et al.

Knowledge gaps and hallucinations are persistent challenges for Large Language Models (LLMs), which generate unreliable responses when lacking the necessary information to fulfill user instructions. Existing approaches, such as Retrieval-Augmented Generation (RAG) and tool use, aim to address these issues by incorporating external knowledge. Yet, they rely on additional models or services, resulting in complex pipelines, potential error propagation, and often requiring the model to process a large number of tokens. In this paper, we present a scalable method that enables LLMs to access external knowledge without depending on retrievers or auxiliary models. Our approach uses constrained generation with a pre-built prefix-tree index. Triples from a Knowledge Graph are verbalized in textual facts, tokenized, and indexed in a prefix tree for efficient access. During inference, to acquire external knowledge, the LLM generates facts with constrained generation which allows only sequences of tokens that form an existing fact. We evaluate our proposal on Question Answering and show that it scales to large knowledge bases (800 million facts), adapts to domain-specific data, and achieves effective results. These gains come with minimal generation-time overhead. ReFactX code is available at https://github.com/rpo19/ReFactX.

Riccardo Pozzi, Valentina Barbera, Renzo Alva Principe et al.

Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp, which can be an extremely effort-consuming task. Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects' mobile phones, and help prosecutors and investigators search into the data and get valuable insights. Our semantic enrichment process involves extracting message data and modeling it using a knowledge graph, generating transcriptions of voice messages, and annotating the data using an end-to-end entity extraction approach. We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search. The proposed approach ensures that users can verify the information by accessing the original data. While we report about early results and prototypes developed in the context of an ongoing project, our proposal has undergone practical applications with real investigation data. As a consequence, we had the chance to interact closely with prosecutors, collecting positive feedback but also identifying interesting opportunities as well as promising research directions to share with the research community.