Common LLM Practitioner Challenges

Model quality depends on the large size of LLM and data used to train it, but training an LLM is quite challenging. Lets learn some common challanges faced while building such LLMs.

1.Training Data Curation

Models which are based on transformers are trained on large datasets of text from multiple data sources. An LLM's quality majorly depends on selection and curation of training data. Preparing the LLM training data is an area of research in LLM industry. Collecting, processing and cleaning the data requires a lot of resources but they are necessary to ensure the quality of model outputs.

2.Large-scale, High-end infrastructure need

While training LLMs, we must maintain the balance between the factors such as model size, model performance, computational complexity, etc. Training requires large-scale accelerated computing resources, high-speed networking and high-end compute instances. This training can take several days to weeks for
completion. The high-end compute instances exist in close quarters to each other and are sometimes grouped in single network spine.
To detect and handle failure, GPU quality management software is essential. It also configures distributed storage and multi-node data I/O for datasets.

3.High Training Costs

To train LLMs, organizations require to invest from millions to billions dollars. Only few organizations are in the position to invest this much money to train their LLMs. Due to this, other teams/organizations look for cost-effective training or to fine-tune the pre-trained models.

4.Machine Learning Expertise

To optimize the performance of LLMs, practitioners use some advanced techniques for distributed training and parallel data processing. Practitioners also manage the framework. It requires expertise in Machine Learning.

5.Responsible AI

LLMs are complex. Understanding their reasoning is a challenging task. Exploratory reaserch is required to make certain that language models are fair, transparent and unbiased. Another area of research is to create certain benchmarks to evaluate and compare the model's performance over various tasks.