Training LLMs comes with several limitations, primarily related to data, computational resources, and ethical considerations. One major challenge is the need for vast amounts of high-quality data. Inadequate or biased data can lead to poor generalization or unintended outputs, limiting the model’s applicability in real-world scenarios.
The computational cost is another significant limitation. Training large models requires powerful hardware like GPUs or TPUs and substantial energy consumption, which can be expensive and environmentally taxing. Additionally, the training process can take weeks or months, depending on the model size and available resources, making it inaccessible to smaller organizations.
Ethical concerns, such as biases in the training data and the potential for misuse, also pose challenges. For example, biased data can lead to models generating harmful or inappropriate content. Addressing these limitations requires careful dataset curation, optimization techniques, and strategies to mitigate biases and environmental impact.