Verifiable Generation

Verifiable generation aims to enhance the trustworthiness of large language models (LLMs) by ensuring their outputs are supported by verifiable evidence, mitigating the problem of factual inaccuracies or "hallucinations." Current research focuses on improving citation accuracy and granularity, developing novel retrieval methods to ensure relevant supporting documents are identified, and designing model architectures that incorporate memory and self-reflection mechanisms to improve the alignment between generated text and its sources. This work is crucial for building more reliable and responsible LLMs, with significant implications for applications requiring high accuracy and transparency, such as scientific writing, legal documentation, and medical diagnosis.