DEV Community

Cover image for Common LLM Practitioner Challenges
MRUGANK MANOJ RAUT
MRUGANK MANOJ RAUT

Posted on

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

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

infra
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

cost
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

ML
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

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.


Interested about how LLMs are trained,then read the following post!


Thank You.

Heroku

This site is built on Heroku

Join the ranks of developers at Salesforce, Airbase, DEV, and more who deploy their mission critical applications on Heroku. Sign up today and launch your first app!

Get Started

Top comments (0)

A Workflow Copilot. Tailored to You.

Pieces.app image

Our desktop app, with its intelligent copilot, streamlines coding by generating snippets, extracting code from screenshots, and accelerating problem-solving.

Read the docs

👋 Kindness is contagious

Immerse yourself in a wealth of knowledge with this piece, supported by the inclusive DEV Community—every developer, no matter where they are in their journey, is invited to contribute to our collective wisdom.

A simple “thank you” goes a long way—express your gratitude below in the comments!

Gathering insights enriches our journey on DEV and fortifies our community ties. Did you find this article valuable? Taking a moment to thank the author can have a significant impact.

Okay