Deterministic Quoting addresses the issue of hallucinations in large language models (LLMs) by ensuring that quotations are exact excerpts from source materials, without any alteration. This technique enhances the reliability of AI in critical applications like healthcare, where accurate information is paramount. By integrating deterministic lookups from a traditional database, it guarantees that quoted text remains unchanged, thereby bridging the trust gap between AI outputs and verifiable data.
Historical Overview: The concept of Deterministic Quoting emerged in 2024 as part of efforts to make AI safer for healthcare applications, driven by the need to eliminate errors in information retrieval tasks where accuracy is crucial.
Key Contributors: Matt Yeung and his team at Invetech are primary contributors to the development and implementation of Deterministic Quoting, focusing on improving AI safety in healthcare settings.
For more details, visit the Deterministic Quoting article.