"Efficient LLM Distillation for NLP Applications"

In a recent presentation at the InfoQ Dev Summit Munich, Ines Montani, creator of spaCy, emphasized the importance of distilling large language models (LLMs) for real-world applications. She highlighted that relying solely on black box models can hinder the development of transparent, explainable, and reliable software.

Montani proposed using transfer learning to extract task-specific information from LLMs, replacing them with distilled components at runtime. This approach enables modular, efficient, and accurate systems.

She also emphasized the value of human involvement in the loop, correcting mistakes and fine-tuning prompts through annotation tools.

Source: https://www.infoq.com/news/2024/10/efficient-mlops-llm-distillation/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global