Knowledge Grounded

Knowledge-grounded approaches aim to enhance natural language processing systems by integrating external knowledge sources, improving the factual accuracy, coherence, and relevance of generated text in tasks like question answering and dialogue. Current research focuses on developing methods for effective knowledge retrieval and integration, often employing retrieval-augmented generation (RAG) models, contrastive learning techniques, and large language models (LLMs) to address challenges like hallucination and the trade-off between specificity and attribution. These advancements are significant for building more reliable and informative AI systems, with applications ranging from improved chatbots and virtual assistants to more trustworthy information retrieval and claim verification tools.