Tenyx aims to fix LLMs’ catastrophic forgetting problem

In the world of large language models (LLMs), fine-tuning them using domain-specific data is crucial for generating relevant outputs. However, this process can lead to “catastrophic forgetting,” where the model loses critical capabilities and reasoning skills. To address this issue, Tenyx is introducing a fine-tuning method that helps businesses adapt LLMs to their unique requirements without sacrificing foundational knowledge or protective safeguards.