Multi FAct

Multi-FAct research centers on evaluating and improving the factuality of large language models (LLMs), particularly in the context of retrieval-augmented generation (RAG) and multilingual applications. Current efforts focus on developing robust evaluation datasets and frameworks, such as those incorporating knowledge graphs and multi-hop reasoning, to assess LLMs' ability to accurately retrieve and synthesize information. This work is crucial for building trustworthy AI systems, improving the reliability of information access, and advancing the development of more accurate and reliable LLMs across diverse languages and domains.