Large Language Models (LLMs), like ChatGPT or Gemini, might seem magical, but their capabilities come from meticulous training and tuning. They are trained on an almost unfathomably large dataset. This imparts in them a broad understanding of language—like someone who’s read an entire library. However, this is just the first step in their creation, as knowing a lot doesn’t make one an expert. Just because someone has read about baking doesn’t mean they can perfectly whip up a four-tier wedding cake. They might still need specific lessons or guidance. That’s where finetuning comes in.
At our company, we specialize in fine-tuning LLMs to make them domain experts. Whether it’s translating complex legal documents or helping businesses communicate seamlessly across languages, we tailor these brilliant models to excel in specific tasks. Let’s dive into the fascinating process behind teaching AI to deliver precision and professionalism.
1. Pre-Training: Laying the Groundwork
Pre-training is the initial phase where an LLM ingests massive amounts of text (think: books, websites, articles) to acquire general language capabilities. It is sort of like letting a curious kid loose in a gigantic library. Eventually, they will have a broad understanding of language, and how the world works. At least, theoretically speaking.
Pre-training isn’t something most companies do themselves—this phase typically requires enormous datasets and compute resources. Instead, organizations use publicly available pre-trained models (e.g., from open-source communities or AI providers) as a starting point.
2. Full-Model Tuning: Overwriting existing Knowledge
Full model finetuning updates all of an LLM’s parameters, by retraining it on a curated domain-specific dataset,to improve performance on a specific task or domain. While this method often yields top performance for a narrow domain, and deeply tailors the model to very specific tasks or jargon, this can lead to the model losing some of its general capabilities if the new data is too narrow.
Full-Model Tuning is also computationally expensive, and requires significant expertise to attain training stability.
To continue our analogy, this is sort of like putting the studious kid in a library consisting entirely of bakery literature. They might become proficient at baking, but it will come at the cost of them forgetting other stuff they learnt in the general library, and performing poorly on non-baking related tasks.
3. Parameter-Efficient Finetuning (PEFT) : Tweaking a Few Knobs
Full-model tuning is often too heavy-handed. Enter PEFT -Full-model tuning is often too heavy-handed. Enter PEFT—a set of methods that updates only a small portion of the model’s parameters, keeping the rest frozen [1].
If full-model finetuning is like re-schooling a child from the ground up, PEFT is more like giving them evening tutoring sessions specifically on how to bake a cake or fix a bike—without overhauling their general education.
3.1. Low-Rank Adaptation (LoRA)
In this method, we insert small, trainable matrices into the model’s layers while the core parameters remain frozen. This reduces the number of trainable parameters, speeding up the process and lowering costs. This method is particularly useful in scenarios where multiple clients require fine-tuned models for different applications, allowing for the creation of specific weights for each use case without the need for separate models [2].
There’s also Quantized LoRA (QLoRA) [3], which is an extended version of LoRA that gives us greater memory efficiency by quantising weight parameters. Typically, LLM parameters are stored in a 32-bit format, but QLoRA compresses them to 4-bit or lower, significantly reducing the memory footprint.
3.2. Prompt Tuning
This technique involves learning a small set of continuous task-specific vectors called “soft prompts” that are prepended to the input embeddings. These learnt prompt tokens shape how the model generates text, leaving most model weights, and its existing knowledge base, untouched.
Prompt tuning allows efficient task switching and can be more interpretable than other fine-tuning methods.
4. Instruction Finetuning
Instruction finetuning is all about training an LLM on examples where it’s explicitly shown how to respond to different instructions or queries. Instead of merely digesting vast swaths of text, the model is guided in a “question-and-answer” or “command-and-response” style. By training the model on these pairs of prompts and responses, the LLM learns a direct mapping between a user instruction and the desired output.
Think of this as giving our well-read “student” a step-by-step guide on proper etiquette and how to respond to specific cues. They already have the knowledge (from the massive “library” they’ve read during pre-training), but now we’re teaching them precisely how to apply that knowledge when someone asks a question or issues a command.
4.1. Reinforcement Learning from Human Feedback (RLHF)
An especially powerful form of instruction fine-tuning is Reinforcement Learning from Human Feedback (RLHF). In RLHF, the model’s outputs are continually rated by humans, and these ratings are used to guide the training process. Think of it as a teacher who reviews the student’s assignments and offers real-time praise or corrections. The model learns which types of responses are preferred (e.g., polite, concise, accurate) and which are not (e.g., rude, incorrect, or irrelevant) [4].
Over multiple rounds of feedback and adjustment, Reinforcement Learning from Human Feedback (RLHF) can produce a model that’s more aligned with human values, brand guidelines, or professional standards. For translation tasks, this means ensuring the output is not only culturally sensitive and regulation-compliant but also tailored to client-specific style preferences.
At EZ, this aligns directly with us — where human-first intelligence meets tech-enabled precision. Our learning algorithms don’t just adapt to feedback — they evolve with it. Every interaction, every edit, and every cultural nuance helps refine the system — making each output smarter, sharper, and more aligned with the standards our clients expect.
There’s more to this story. Stay tuned for Part 2 — where we dive deeper into how RLHF powers localization at scale.
References:
- Xu, Lingling, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang. "Parameter-efficient fine-tuning methods for pretrained language models: A critical review and assessment." arXiv preprint arXiv:2312.12148 (2023).
- Hu, Edward J., Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021).
- Dettmers, Tim, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. "Qlora: Efficient finetuning of quantized llms." Advances in Neural Information Processing Systems 36 (2024).
- Li, Zihao, Zhuoran Yang, and Mengdi Wang. "Reinforcement learning with human feedback: Learning dynamic choices via pessimism." arXiv preprint arXiv:2305.18438 (2023).