5 Problems Encountered Fine-Tuning LLMs with Solutions

Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction. By tailoring model parameters on domain-specific data, practitioners can achieve improved accuracy, specialized reasoning, and more relevant outputs. While this can be an advantageous approach in improving model performance for specific applications, it is not exempt from problems that may be encountered along the process.

This article presents five problems that one may encounter in the fine-tuning process and how to navigate them.

1. Catastrophic Forgetting

As catastrophic as it may sound, this problem can, in fact, happen. Catastrophic forgetting arises when an LLM being fine-tuned loses part of its previously learned language capabilities upon being exposed to new data. The problem is generally caused by the internal neural network layers’ tendency to overwrite old parameters or weights during the process of learning new information. When fine-tuning on specialized domain data, the LLM might even sacrifice its broad language skills for gaining narrow expertise, which might be problematic.

The good news: there are techniques like rehearsal methods and Elastic Weight Consolidation (EWC) that help alleviate this problem, for instance, by periodically showing samples from the original dataset to the model during fine-tuning.

Here’s a simple example of what rehearsal could look like in practice.

2. Issues with Training Data Quality

When data used for fine-tuning are low-quality or biased, it can lead to LLM performance degradation and bias accentuation. The model may inherit flaws from training data containing inconsistencies or factual errors. Since fine-tuning generally uses much smaller datasets than pre-training, these problematic data examples have a greater impact on the model being fine-tuned.

The solution for this problem is to implement rigorous data curation, cleaning, and quality-check processes, along with data augmentation to seek a diverse, balanced, bias-free, and high-quality dataset.

Here is a simple pseudocode example of parameter freezing with EWC.

Note that Fisher information identifies which parameters are most crucial for prior tasks, allowing EWC to selectively resist changing them during fine-tuning.

3. Computational Expense

The most widespread problem when fine-tuning LLMs is arguably that, despite using a significantly smaller dataset than those vast ones used for pre-training general-purpose LLMs, the process still requires significant computational resources, particularly for larger models with millions to billions of parameters. The cost of fine-tuning a state-of-the-art LLM, for instance, can run into thousands of dollars, thereby drastically limiting aspects like experimentation and access to fine-tuning capabilities for smaller organizations.

Parameter-efficient fine-tuning approaches like LoRA (Low-Rank Adaptation) and prefix-tuning were proposed to partly reduce this intensive requirement while achieving reasonable fine-tuning results.

Here is a piece of code for parameter efficient tuning (PEFT) using LoRA.

4. Overfitting

A standout among the classics that can affect any and every single machine learning and deep learning model, overfitting is also present in the realm of LLM fine-tuning: it occurs when the model excessively memorizes the training examples, failing to learn generalizable patterns from them, which severely limits its practical effectiveness in real-world scenarios where the model receives new data never seen before.

Techniques to counter overfitting in deep neural networks like early stopping, dropout, and other regularization strategies can help prevent this common issue during LLM fine-tuning.

Here is an example of preventing overfitting in Trainer with early stopping.

Note: We will assume that there is an existing model in place already.

5. Alignment Challenges

This problem relates to the challenge of ensuring the model abides by human values and avoids harmful results after being fine-tuned. Fine-tuning can sometimes inadvertently dismantle alignment properties that were solidly built during pre-training, yielding a fine-tuned model that may generate inappropriate or even unethical language in some domains.

Luckily, techniques like Reinforcement Learning from Human Feedback (RLHF), together with Constitutional AI, have proved themselves useful in helping maintain LLM alignment with human and ethical aspects.

Summary & Next Steps

In summary, effective fine-tuning requires balancing adaptation to new domains with preservation of prior capabilities, mitigating data issues, controlling costs, preventing overfitting, and ensuring alignment.

As next steps, practitioners should:

1. monitor for catastrophic forgetting by evaluating on both original and domain-specific benchmarks
2. establish robust data pipelines for ongoing curation and augmentation
3. experiment with parameter-efficient methods (LoRA, prefix-tuning) to reduce compute and cost
4. apply early stopping and regularization during training to maintain generalization
5. integrate RLHF or Constitutional AI workflows to safeguard alignment