The paper introduces a novel, flexible framework -Pareto optimal self-supervision, which utilizes programmatic supervision to calibrate and rectify errors inherent in LLMS, thereby eliminating the necessity for additional manual intervention. However, the issue of hallucination remains a prominent challenge, particularly in real-world applications where a high degree of accuracy and reliability is expected.Īddressing this issue, a research team from Microsoft proposed an innovative solution in a new paper Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision. Large language models (LLMs) have undoubtedly displayed immense potential in the field of natural language processing and understanding.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |