8 citations · 10 across the 10 of their papers we have counts for
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ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generation
Riccardo Pozzi, Matteo Palmonari, Andrea Coletta +3
Knowledge gaps and hallucinations are persistent challenges for Large Language Models (LLMs), which generate unreliable responses when lacking the necessary information to fulfill…
Aligning Knowledge Graphs and Language Models for Factual Accuracy
Nur A Zarin Nishat, Andrea Coletta, Luigi Bellomarini +3
Large language models like GPT-4, Gemini, and Claude have transformed natural language processing (NLP) tasks such as question answering, dialogue generation, summarization, and so…
Fine-tuning Large Enterprise Language Models via Ontological Reasoning
Teodoro Baldazzi, Luigi Bellomarini, Stefano Ceri +3
Large Language Models (LLMs) exploit fine-tuning as a technique to adapt to diverse goals, thanks to task-specific training data. Task specificity should go hand in hand with domai…