Continuous improvement
In the realm of LLMs such as GPT, continuous improvement is paramount to maintaining their relevance and efficacy. This process is multifaceted and involves integrating new data, developing strategies to prevent previous knowledge from being forgotten, and incorporating human feedback.
Firstly, incorporating newer data is essential as language and societal contexts evolve. Regular updates with recent datasets ensure that models grasp current terminologies, phrases, and relevant topics. This could involve data from diverse sources, such as the latest news articles, scientific publications, and trending internet content. Such updates help the model stay current with linguistic trends and societal changes.
Secondly, a significant challenge in training neural networks, especially when they learn new information, is avoiding catastrophic forgetting. This phenomenon occurs when a model loses the information it had learned previously upon acquiring new knowledge...
