Knowledge distillation
Knowledge distillation is a technique that transfers the abilities of a large model (teacher) to a small model (student). This is a term that often appears in the low-cost model debate.
Knowledge distillation is a technique that transfers the capabilities of a large, high-performance model (teacher) to a small, lightweight model (student). The student model learns by imitating the teacher model's answer method, which can be likened to studying with key notes that summarize a lecture by a famous lecturer.
Because large models have good performance but are expensive and slow to operate, they are widely used to lower costs by creating small models with similar skills. It is a key technology when creating on-device AI or low-cost API models that run on smartphones.
Meanwhile, suspicions that low-cost models were created by receiving and learning the output of other companies' top models without permission have spread into industry controversy. The rules for how much is legitimate learning and where it is free riding are still being worked out.
✅ Why it matters
- It allows you to utilize the performance of large models at low cost and high speed
- It is a core technology for lightweight AI that runs directly on devices such as smartphones
- It is the key to understanding the low-cost, high-performance model debate news
⚠️ Limits and debates
- Student models cannot fully match the performance of teacher models
- They can also inherit errors and biases from teacher models
- There is an ongoing debate about the legitimacy of unauthorized distillation of third-party model outputs