Massive Language Models (MLM) like GPT have made a significant impact in the field of natural language processing (NLP). Tools such as ChatGPT utilize these models to generate coherent and contextually relevant responses.
These models are trained on large volumes of data and are then fine-tuned on specific datasets to improve their performance in particular tasks.
Challenges and Limitations
- Training Time & Cost: Can be extremely lengthy and requires high-performance GPUs/TPUs.
- Data Quality: Needs to be as clean and consistent as possible to avoid garbage-in-garbage-out.
- Diminishing Returns: Adding more data does not always result in significant improvements.
- Bias & Fairness: Crucial to address biases inherent in human language data.
By addressing these challenges, MLMs can be adapted to specific applications, improving their relevance and accuracy in different contexts.