Source Attribution for Large Language Models

AbstractAs Large Language Models (LLMs) become more widely used for tasks like document summarization, question answering, and information extraction, improving their trustworthiness and interpretability has become increasingly important. One key strategy for achieving this is extbfattribution, a process that tracks the sources of the generated responses. This tutorial will explore various attribution techniques, including model-driven attribution, post-retrieval answering, and post-generation attribution. We will also discuss the challenges involved in implementing these approaches, and also look at the advanced topics such as model-based attribution for complex cases, table attribution, multimodal attribution, and multilingual attribution.