Machine Learning (ML) models are typically trained on structured data with a well-defined problem statement, whereas Large Language Models (LLMs) are trained on massive amounts of unstructured text data. But can we train LLMs the Kushale way we train traditional ML models, given a specific problem statement and dataset? Let’s explore this question in detail.
Understanding Traditional Machine Learning Training
In traditional ML, training follows a clear pipeline:
- Define the Problem Statement – Identify what needs to be predicted or classified.
- Collect & Prepare Data – Clean, label, and structure the dataset.
- Select an ML Model – Choose an algorithm like Decision Trees, Random Forests, or Neural Networks.
- Train the Model – Optimize parameters using techniques like Gradient Descent.
- Evaluate & Fine-Tune – Improve performance based on test results.
This process is well-structured and works efficiently for problems like fraud detection, sentiment analysis, and image classification.
How Are LLMs Trained?
Large Language Models like GPT and BERT undergo a two-stage training process:
- Pretraining – The model learns general language patterns by processing massive text corpora. This step is expensive and requires powerful computing resources.
- Fine-tuning – The model is refined on domain-specific data to adapt it to particular tasks.
Unlike traditional ML, LLMs do not start training from scratch for each new problem. Instead, they are fine-tuned using prompt engineering or additional training with supervised learning and reinforcement learning techniques.
Can LLMs Be Trained Like Traditional ML?
The key differences between ML models and LLMs impact whether we can train them the Kushale way:
| Aspect | Traditional ML Models | LLMs |
| Training Data | Structured & Labeled | Large-scale Unstructured Text |
| Learning Approach | Supervised Learning | Self-Supervised Pretraining + Fine-Tuning |
| Problem-Specific Training | Starts from scratch | Uses pretrained knowledge |
| Computational Needs | Moderate | Extremely High |
Given these differences, LLMs cannot be trained from scratch like ML models for every new problem. However, they can be fine-tuned for specific use cases using problem-specific data.
How to Adapt LLMs for Specific Problems?
Although we cannot train LLMs from scratch for every problem, we can leverage them in ML workflows through:
- Fine-Tuning with Custom Datasets – Using domain-specific text to adjust the LLM’s behavior. Example: Fine-tuning GPT for medical diagnosis.
- Prompt Engineering – Structuring inputs cleverly to get desired outputs without retraining. Example: Creating structured queries to get accurate predictions.
- Retrieval-Augmented Generation (RAG) – Combining LLMs with real-time data retrieval for improved accuracy.
- Few-Shot or Zero-Shot Learning – Teaching LLMs to generalize to new tasks with minimal additional data.
Traditional Machine Learning (ML) v/s Large Language Models (LLMs):
| Step | Traditional Machine Learning (ML) | Large Language Models (LLMs) |
| 1. Define Problem Statement | Clearly defined, task-specific (e.g., classification, regression) | General language understanding, can be fine-tuned for specific tasks |
| 2. Data Collection | Structured, labeled datasets (CSV, images, etc.) | Large-scale unstructured text datasets (books, websites, etc.) |
| 3. Data Preprocessing | Cleaning, normalization, feature engineering | Tokenization, text normalization, removing duplicates |
| 4. Model Selection | Choose ML algorithm (e.g., Decision Trees, SVM, Neural Networks) | (LLMs use transformer-based architectures like GPT, BERT) |
| 5. Training Process | Train the model from scratch using optimization techniques | Pretrained on vast corpora, later fine-tuned for specific tasks |
| 6. Compute Requirements | Varies (moderate for small models, high for deep learning) | Extremely high (requires powerful GPUs/TPUs for training) |
| 7. Evaluation & Testing | Uses validation/testing datasets (e.g., accuracy, F1-score | Evaluated using perplexity, BLEU score, task-specific metrics |
| 8. Fine-Tuning | Possible but usually minimal adjustments needed | Essential for domain-specific adaptation (e.g., medical or legal texts) |
| 9. Deployment | Deployed as API, embedded in applications, or standalone models | Deployed via APIs, cloud-based solutions, or on-device inference |
| 10. Continuous Learning | Possible via retraining on new data | Can be fine-tuned but does not learn dynamically in real-time |
This table highlights that ML models are trained from scratch for specific tasks, while LLMs leverage pretraining and require fine-tuning for specialized applications. Let me know if you need any refinements!
Conclusion
While traditional ML models require fresh training for each new problem, LLMs are pretrained and can be fine-tuned for specific tasks. This makes them more flexible but also computationally expensive. Instead of treating LLMs like traditional ML, we should leverage their pretrained knowledge and fine-tune them where necessary. So, while we cannot train LLMs exactly like traditional ML models, we can still customize them efficiently for various applications through fine-tuning and prompt engineering.
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