๐๐ฉ๐ข๐ฌ๐จ๐๐ ๐๐: ๐๐จ๐ฐ ๐ญ๐จ t๐๐ฌ๐ญ “๐๐๐ง๐๐ซ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง” ๐๐จ๐ซ ๐๐ซ๐๐๐ญ๐ข๐ง๐ ๐ ๐๐ฎ๐ฌ๐ข๐ง๐๐ฌ๐ฌ ๐๐จ๐ฆ๐๐ข๐ง ๐๐ฑ๐ฉ๐๐ซ๐ญ ๐ฐ๐ข๐ญ๐ก ๐๐๐๐ฌ, ๐ก๐๐ฅ๐ฅ๐ฎ๐๐ข๐ง๐๐ญ๐ข๐จ๐ง-๐๐ซ๐๐!
The last three episodes of our series required a solid foundation in data science and model training. If you’ve been following along, hereโs a quick checklist of the prerequisites needed to get the most out of this post:
I. Pre-requisites for this post:
- Understanding how to fine-tune a large language model (LLM)
- Building a balanced dataset with domain-specific knowledge (1)
- Adjusting hyperparameters effectively (1)
- Recognizing and mitigating data biases (1)
Now that weโre all caught up, letโs dive in!
In the framework we’ve developed, achieving 90% accuracy is a solid threshold for generalization. Keep in mind, that figure could vary depending on how strict your client or supervisor is. Here’s the process: for every stage of the model training, we should aim to run 100 tests per step. If 90% of those tests hit the expected result, weโve successfully passed that step.
II. Step 1: Direct preference optimization (DPO)
For the first step, it is recommended to use Direct Preference Optimization (DPO). You might be tempted to go for LoRa, but as discussed in Episode 10, that approach won’t achieve generalization unless artificial neural networks (ANN) are introduced during training.
III. Step 2: Checking data understanding
Once enough data has been gathered, the model should start to generalize. However, generalization is only the first part of this step. At this point, itโs not yet assessing reasoning abilities, but rather ensuring it understands when and how to adjust output variables based on the input.
IV. Step 3: Explanation Tuning
Now itโs getting to the crux of it. For this step, it needs to dive into Explanation Tuning. Although this concept has been around since GPT-2, it deserves recognition for improving model robustness. A key resource on this is the 2023 paper, Explanation-based Fine-tuning Makes Models More Robust to Spurious Cues by Ludan et al.
In practical terms, explanation tuning means adding context-specific explanations to the “dynamic system message” for each sample. This differs from the general system message used in traditional fine-tuning. The goal is to make the modelโs behavior more interpretable and trustworthy, thereby enhancing its usefulness in business applications.
V. Wrapping up Generalization
By following these steps, it will achieve generalization in a more effective and structured way. Stay tuned for more insights on model training and optimization!
Disclaimer
While explanation tuning is excellent for improving robustness (by reducing the model’s reliance on spurious correlations), it isn’t the primary tool for achieving generalization. Generalization is more reliant on diverse and representative training data. That said, adding explanations enhances the modelโs interpretability, which is key for business contexts.
(1) This post assumes you know how to train a model effectively, avoiding overfitting, underfitting, and managing essential tasks like feature selection, hyperparameter tuning, dataset balancing, and adjusting training steps/epochs. It also assumes you’re familiar with the right model depth for your task and are aware of key issues like model capacity and gradient stability. Additionally, it assumes you can identify and account for data bias, particularly in the 10% of cases where the model might fail.