Abstract
Systems and methods can refine LLM outputs through a pipeline architecture that integrates human expert feedback. This pipeline may feature a dynamic routing engine to assign detected errors to qualified experts, an adaptive quality-weighted consensus engine to validate proposed corrections, and an impact attribution engine to measure the value of contributions. These components can operate in a feedback loop to identify, correct, and reintegrate validated knowledge. This process can be used to ground future LLM generations with curated information, potentially affecting the factual accuracy and reliability of model outputs in a scalable manner.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Priya, Saloni, "Pipeline for LLM Refinement with Expert Routing, Consensus, and Impact Attribution", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10947