Aligning Knowledge Graphs and Language Models for Factual Accuracy

--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 forth; yet their susceptibility to hallucination stands as one of the major challenges. Among numerous approaches to overcome this challenge, integration of Knowledge Graphs (KGs) into language models has emerged as a promising solution as it provides structured, reliable, domain-specific, and up-to-date external information to the language models. In this paper, we introduce ALIGNed-LLM, a simple yet effective approach to improve language models' factuality via a lean strategy to infuse KGs into the latent space of language models inspired by LLaV A where visual and textual information is infused. We use embeddings from a pre-trained Knowledge Graph Embedding (KGE) model, such as TransE, and a trainable projection layer to align entity and text embeddings. This alignment enables the language model to distinguish between similar entities improving factual grounding and reducing hallucination. We tested our approach on three popular questions-answering benchmark datasets alongside language models of varying sizes, showing significant improvement. Furthermore, we applied our approach to a real-world financial use case from a large central bank in Europe, which demands high accuracy and precision, demonstrating a substantial improvement of the LLM answers. The emergence of Large Language Models (LLMs) such as GPT -4 [27], Gemini [1], Llama [36], and Claude [2], is producing a performance revolution across natural language processing tasks, such as question answering, dialogue generation, summarization, and many more. LLMs have also confirmed the universality of human language, showing their potential to build domain-specific assistants, trained to follow natural language instructions and accomplish various tasks end to end [22]. The opinions expressed in this paper are personal and should not be attributed to Banca d'Italia. The work was done outside Amazon.