Sunday morning chat with deepseek on how llms learn from each other

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Me: Explain in plain english how an LLM learns from another LLM using the concept of distilation

DeepSeek: Thought for 62 seconds

Okay, so I need to explain how an LLM learns from another LLM using the concept of distillation in plain English. Let me start by breaking down what I know about this.

First, I remember that LLM stands for Large Language Model, like GPT-3 or similar. These models are trained on vast amounts of text data and can generate human-like text. Now, distillation… I think that’s a term from the process where a larger model’s knowledge is transferred to a smaller one. The idea is that the smaller model can perform almost as well as the larger one but is more efficient.

So, how does that work exactly? Maybe the bigger model generates some outputs, and the smaller model learns from those outputs instead of the original data? That makes sense because training a big model is resource-heavy, and if a smaller model can mimic it, that’s beneficial. But wait, why is it called distillation? Maybe like distilling the essential knowledge from the larger model into a smaller one.

Let me think of an analogy. If the large model is a teacher, the smaller one is a student. The teacher’s knowledge is transferred to the student through the answers the teacher provides. The student learns by trying to imitate the teacher’s answers. So, instead of learning from raw data, the student learns from the teacher’s processed responses. That way, the student can capture the important patterns without needing the same computational resources.

But how is this process structured? I think the teacher model generates outputs, maybe in the form of probabilities over possible next words (like in text generation), and the student model is trained to match those probabilities. This is different from training the student on the original hard labels (like correct answers in a dataset) because the teacher’s outputs are softer, more nuanced distributions. This soft target might contain more information, helping the student generalize better.

Wait, in traditional training, the model is trained to predict the next word based on the training data. In distillation, instead of using the actual next word from the data, the student model tries to match the probabilities output by the teacher model. So the loss function would compare the student’s output probabilities to the teacher’s, not to the ground truth. That way, the student learns the teacher’s “style” or the way the teacher makes predictions.

But maybe sometimes it’s a combination of both? Like using both the ground truth and the teacher’s outputs. That might help the student not only mimic the teacher but also stay grounded in the actual correct answers. That could prevent the student from picking up any mistakes the teacher might make, although if the teacher is accurate, maybe that’s less of an issue.

Another thing is temperature in distillation. I remember that when generating soft targets, a temperature parameter is used to smooth the probability distribution. Higher temperatures make the distribution softer, which might help the student learn the relationships between different classes or tokens more effectively. The student is then trained using these softened probabilities, which can carry more information than the hard targets.

So putting this all together, the process would involve running the teacher model on a dataset (maybe the original training data or a new one) to generate predictions. These predictions are then used as targets for the student model. The student is trained to minimize the difference between its own predictions and the teacher’s. This way, the student becomes a smaller, more efficient version that still captures the teacher’s knowledge.

Wait, but why not just train the student on the original data? Because the teacher model has already learned complex patterns and nuances from the data, which might be difficult for the student to learn directly, especially if the student has a smaller capacity. By learning from the teacher’s outputs, the student can focus on the distilled knowledge that’s already processed and perhaps more structured.

Also, this method can be useful when the original training data isn’t available, and only the teacher model is accessible. The student can still learn from the teacher’s outputs without needing the original dataset, which might be proprietary or too large to handle.

Are there any potential downsides? If the teacher model has biases or errors, the student might inherit them. Also, the student’s performance is inherently limited by the teacher’s knowledge. But if the teacher is a high-performing model, the student can still achieve good performance with fewer resources.

So, in summary, distillation in the context of LLMs involves training a smaller student model to imitate the outputs of a larger teacher model. The teacher generates soft targets (probabilistic outputs) which the student learns from, allowing the student to capture the teacher’s knowledge in a more efficient form. This process helps in creating models that are faster and require less computational power while maintaining much of the original model’s effectiveness.

The DeepSeek formal response:

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How an LLM Learns from Another LLM Using Distillation (in Plain English):

Imagine you have a brilliant, experienced teacher (a large, complex LLM) and a student (a smaller, simpler LLM). The teacher knows a lot but is slow and resource-heavy. The student needs to learn quickly and work efficiently. Here’s how distillation works:

1. The Teacher Demonstrates:
   The teacher answers questions or completes tasks, but instead of just giving a single “correct” answer, it provides probabilistic outputs—like showing the student not just the answer, but also the confidence in different options. For example, if asked to translate “hello,” the teacher might output probabilities like:
   • “hola” (70%)”bonjour” (20%)”ciao” (10%).
   These nuanced outputs are called soft targets.
2. The Student Imitates:
   The student learns by trying to mimic the teacher’s soft targets. Instead of learning from raw data (e.g., memorizing vocabulary), it focuses on the teacher’s reasoning patterns. This is like the student learning not just what the teacher knows, but how the teacher thinks.
3. Smoothing the Knowledge (Temperature):
   To make the teacher’s outputs even more informative, a “temperature” setting is used. Higher temperatures soften the probabilities, making the differences between options less sharp (e.g., turning 70%/20%/10% into 50%/30%/20%). This helps the student grasp broader patterns (e.g., “hola” and “bonjour” are both valid greetings), not just memorization.
4. Efficiency Gains:
   Over time, the student becomes a compact version of the teacher. It retains most of the teacher’s skills but uses less computational power and memory, making it faster and cheaper to run—ideal for devices like phones or apps.

Why It Works:

• The teacher’s soft targets contain richer information than raw data (e.g., “bonjour is sometimes right, but hola is better here”).
• The student avoids reinventing the wheel; it builds on the teacher’s expertise.

Trade-offs:

• The student might inherit the teacher’s mistakes or biases.
• It can’t surpass the teacher’s knowledge but balances performance and efficiency well.

Analogy:
It’s like a chef (student) learning from a master chef (teacher) by tasting their dishes and reverse-engineering the recipes, rather than starting from scratch with raw ingredients. The student captures the essence of the teacher’s craft more efficiently.