Microsoft’s new Pareto Optimal Self-Supervision Framework Automatically Corrects Language Models to Boost GPT SOTA Records

In a new paper Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision, a Microsoft team research team presents Pareto optimal self-supervision, a flexible framework that leverages programmatic supervision to automatically calibrate and correct error for Large language models without extra manual efforts.

ai artificial intelligence boost deep-neural-networks error framework gpt language language model language models large language large language models machine learning machine learning & data science microsoft ml nature language tech paper pareto programmatic records research research team sota supervision team technology