Abstract
The present disclosure discloses an improved system and method for GenAI/LLM enabled payload generator for feature enrichment, selection and optimization. The present disclosure discloses a pipeline to apply fine-tuned large language model (LLM) to help feature simulators to test system performance and robustness. These templates may be incorporated into a multithreaded emulator to produce payloads that encompass a wide range of feature distributions, including high-volume and outlier cases. The Large Language Model (LLM) may be fine-tuned using a reinforcement learning feedback loop to detect potential issues in the pipeline. If a discrepancy arises between the business description and the feedback, a GenAI-enabled adversarial attack may be employed to test corner cases, ensuring robustness and alignment with business objectives.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
He, Runxin; Lou, Mingji; Jagtap, Shayaan; and Rayapati, Ajay Raman, "GenAI ENABLED PAYLOAD GENERATOR FOR FEATURE ENRICHMENT, SELECTION AND OPTIMIZATION", Technical Disclosure Commons, (October 03, 2024)
https://www.tdcommons.org/dpubs_series/7400