Mastering Large Language Model Techniques: A Comprehensive Guide
Summary
Large language models (LLMs) are a class of generative AI models that can recognize, summarize, translate, predict, and generate language using very large datasets. Training these models is challenging and requires various techniques to customize them for specific tasks. This article covers the main ideas behind LLM techniques, including prompt engineering, prompt learning, parameter-efficient fine-tuning (PEFT), fine-tuning, chain-of-thought reasoning, system prompting, and reinforcement learning with human feedback (RLHF).
Understanding LLM Techniques
LLMs are built using transformer networks and can be customized using various techniques. Here are some of the main techniques:
Prompt Engineering
Prompt engineering involves manipulating the prompt sent to the LLM without altering the model’s parameters. This technique is light in terms of data and compute requirements. It works well for tasks that require specific instructions or context.
Prompt Learning
Prompt learning uses prompt and completion pairs to impart task-specific knowledge to LLMs through virtual tokens. This process requires more data and compute than prompt engineering but provides better accuracy.
Parameter-Efficient Fine-Tuning (PEFT)
PEFT introduces a small number of parameters or layers to the existing LLM architecture and trains them with use-case-specific data. This technique provides higher accuracy than prompt engineering and prompt learning but requires more training data and compute.
Fine-Tuning
Fine-tuning involves further training a pre-trained LLM using a task-specific dataset to adapt it to the task. This technique provides high accuracy but requires a large amount of training data and compute.
Chain-of-Thought Reasoning
Chain-of-thought reasoning is a prompt engineering technique that helps LLMs improve their performance on multi-step tasks. It involves breaking a problem down into simpler steps with each step requiring slow and deliberate reasoning.
System Prompting
System prompting involves adding a system-level prompt in addition to the user prompt to provide specific and detailed instructions to the LLM. The quality and specificity of the system prompt can have a significant impact on the relevance and accuracy of the LLM’s response.
Reinforcement Learning with Human Feedback (RLHF)
RLHF is a customization technique that enables LLMs to achieve better alignment with human values and preferences. It uses reinforcement learning to enable the model to adapt its behavior based on the feedback it receives.
Parameter-Efficient Fine-Tuning Techniques
PEFT techniques use clever optimizations to selectively add and update few parameters or layers to the original LLM architecture. Here are some of the main PEFT techniques:
IA3
IA3 adds even fewer parameters compared to adapters, which simply scale the hidden representations in the transformer layer using learned vectors. These scaling parameters can be trained for specific downstream tasks.
LoRA
LoRA injects trainable low-rank matrices into transformer layers to approximate weight updates. Instead of updating the full pre-trained weight matrix, LoRA updates its low-rank decomposition, reducing the number of trainable parameters and GPU memory requirements.
SFT with Instructions
SFT with instructions leverages the intuition that NLP tasks can be described through natural language instructions. This method combines the strengths of fine-tuning and prompting paradigms to improve LLM zero-shot performance at inference time.
Reinforcement Learning with Human Feedback
RLHF is a three-stage fine-tuning process that uses human preference as the loss function. The SFT model fine-tuned with instructions is considered the first stage in the RLHF technique. The SFT model is trained as a reward model (RM) in stage 2 of RLHF, and stage 3 focuses on fine-tuning the initial policy model against the RM using reinforcement learning with a proximal policy optimization (PPO) algorithm.
Table: Comparison of LLM Techniques
| Technique | Description | Data Requirements | Compute Requirements | Accuracy |
|---|---|---|---|---|
| Prompt Engineering | Manipulates the prompt sent to the LLM | Low | Low | Medium |
| Prompt Learning | Uses prompt and completion pairs to impart task-specific knowledge | Medium | Medium | High |
| PEFT | Introduces a small number of parameters or layers to the existing LLM architecture | High | High | High |
| Fine-Tuning | Further trains a pre-trained LLM using a task-specific dataset | High | High | High |
| Chain-of-Thought Reasoning | Breaks a problem down into simpler steps with each step requiring slow and deliberate reasoning | Low | Low | Medium |
| System Prompting | Adds a system-level prompt in addition to the user prompt | Low | Low | Medium |
| RLHF | Uses reinforcement learning to enable the model to adapt its behavior based on human feedback | High | High | High |
Table: Comparison of PEFT Techniques
| Technique | Description | Data Requirements | Compute Requirements | Accuracy |
|---|---|---|---|---|
| IA3 | Adds even fewer parameters compared to adapters | Medium | Medium | High |
| LoRA | Injects trainable low-rank matrices into transformer layers | Medium | Medium | High |
| SFT with Instructions | Combines the strengths of fine-tuning and prompting paradigms | High | High | High |
Conclusion
Mastering LLM techniques requires understanding various customization techniques, including prompt engineering, prompt learning, PEFT, fine-tuning, chain-of-thought reasoning, system prompting, and RLHF. By choosing the right technique for the task, developers can improve the accuracy and efficiency of LLMs. This article provides a comprehensive guide to LLM techniques, including PEFT techniques and RLHF.