Exploring Techniques for Utilizing and Finetuning Pretrained Large Language Models (LLMs)


In the sphere of natural language processing (NLP), Pretrained Large Language Models (LLMs) such as BERT, GPT, and Llama have become foundational tools. Their versatility and robust performance across various tasks make them indispensable. As these models become more integrated into practical applications, understanding the various methods for utilizing and finetuning them is crucial. This blog provides a comprehensive overview of these methods, as well as answers to some key questions about their practical applications.

Methods for Utilizing and Finetuning LLMs

Feature-Based Approach

The Feature-Based Approach uses the pretrained LLM as a feature extractor. Here, the LLM generates embeddings from input data, which are then used to train a downstream model like a linear classifier. This approach is highly efficient as it doesn't require finetuning the LLM, and the embeddings can be precomputed for further analysis. Although this method is straightforward, it has proven effective in numerous scenarios where rapid deployment is necessary without the computational cost of full model training.


There are two primary methods of finetuning LLMs: Selective Finetuning and Complete Finetuning.

Selective Finetuning

Selective Finetuning involves adding new output layers to the LLM and updating only these layers, keeping the main body of the LLM frozen. This method strikes a balance between computational efficiency and performance enhancement, making it suitable for tasks where complete model retraining is unnecessary.

Complete Finetuning

Complete Finetuning, on the other hand, involves training additional layers and updating all parameters in the LLM. While this approach is more computationally expensive, it can significantly improve performance, especially for domain-specific tasks. Complete finetuning tailors the model to the specific nuances of a given dataset, maximizing its efficacy in specialized contexts.

In-Context Learning and Prompting

In-Context Learning and Prompting are techniques that allow models to adapt to new tasks without modifying model parameters.

In-Context Learning

In-Context Learning involves demonstrating tasks within the input prompt, enabling the model to infer and perform the task autonomously. This method is exceptionally useful when labeled data is scarce or unavailable, as it leverages the model's existing knowledge to tackle new challenges.

Prompt Tuning

Prompt Tuning, conversely, involves tweaking the input prompts to improve model performance. This method doesn't require parameter updates, but it does demand manual efforts to optimize the prompts for specific tasks.

LLM Indexing

LLM Indexing involves an indexing module that parses and stores document chunks with vector embeddings in a database. This facilitates efficient information retrieval from external sources, extending the capabilities of the LLM to handle large datasets and complex queries. Indexing provides a robust mechanism for managing and accessing vast amounts of information quickly and accurately.

Parameter-Efficient Finetuning

Parameter-efficient finetuning methods offer a middle ground between full finetuning and zero-shot learning. They include:

Soft Prompt Tuning

Soft Prompt Tuning uses trainable parameter tensors appended to the input embeddings to adjust the model's performance. This technique allows for a more flexible and fine-grained adjustment of model behavior without extensive retraining.

Prefix Tuning

Prefix Tuning attaches trainable tensors to the input at each transformer block. By doing so, it modulates the entire processing flow of the input data through the model, offering another layer of control over the model's responses.

Adapter Methods

Adapter methods add new, smaller fully connected layers within the transformer. Only these additional layers are trained, significantly reducing the computational burden while still achieving notable performance improvements.

Low-Rank Adaptation (LoRA)

LoRA updates the weights of the model using low-rank matrices. This approach provides a compact and efficient method for model adaptation, achieving substantial performance gains without the full computational expense of complete finetuning.

Reinforcement Learning with Human Feedback (RLHF)

RLHF combines supervised learning with reinforcement learning to align the model's output with human preferences. Human feedback is translated into a reward model that guides the LLM adaptation process, ensuring that the model's responses align more closely with desired outcomes. This method is particularly effective for refining LLMs in conversational settings, where human-like understanding and interaction are paramount.

Answering Key Questions

1. How do soft prompt tuning and prefix tuning differ in their implementations and effects on the model?

Soft Prompt Tuning and Prefix Tuning are both strategies for parameter-efficient finetuning, yet they have different implementations and impacts on the model. Soft Prompt Tuning adjusts the input embeddings by appending trainable tensor parameters, offering a flexible adjustment mechanism directly at the input layer. Prefix Tuning, however, involves attaching trainable tensors at each transformer block, influencing the model's internal processes from the beginning of the input sequence all the way through the model. This results in Prefix Tuning having a more profound impact on the model's behavior as it influences the model at multiple stages rather than just at the input level.

2. What specific scenarios or tasks are best suited for in-context learning compared to traditional finetuning?

In-Context Learning is particularly advantageous in scenarios where labeled data is limited or unavailable. Traditional finetuning relies heavily on labeled datasets to adjust the model parameters, which may not always be feasible. In-Context Learning bypasses this requirement by using the input prompt to demonstrate the task. It's highly effective in tasks like one-shot or few-shot learning scenarios, where the model is expected to generalize from very few examples. Additionally, it's beneficial in rapid prototyping and experimentation, allowing developers to quick-test the model's capabilities on new tasks without the overhead of collecting extensive datasets and performing model retraining.

3. How does Low-Rank Adaptation (LoRA) improve efficiency without significantly compromising on performance compared to full finetuning?

LoRA improves efficiency by updating the model weights using low-rank matrices. This method reduces the number of parameters that need to be trained, significantly cutting down on computational resources and time required for training. Despite this reduction, LoRA manages to maintain a level of performance comparable to full finetuning, thanks to its intelligent parameter adjustment which focuses on critical weight updates that impact the model's performance the most. This balance of efficiency and performance makes LoRA an attractive option for scenarios where computational resources are limited, but high model performance is still desired.


The versatility and power of Pretrained Large Language Models (LLMs) can be harnessed through various methods tailored to specific needs and constraints. From feature-based approaches and finetuning techniques to advanced methods like in-context learning, parameter-efficient finetuning, and reinforcement learning with human feedback, each strategy offers unique advantages. Understanding these methods allows practitioners to choose the most suitable approach for their specific NLP tasks, ensuring optimal performance and efficiency.