Traditional in-context learning for LLMs relies on carefully curated examples, but what if the model could generate its own demonstrations? An idea called Self-Generated In-Context Learning SG-ICL explores this possibility.
How SG-ICL solves the problem of traditional LLM task setting
Typically, when we use LLMs, we rely on a process called in-context learning. We feed the model examples to demonstrate the task we want it to do. These examples are critical – bad ones mean poor results. It can be a lot of work to find the right data, and you never know if what you choose will ultimately lead to the best outcome.
Instead of looking for examples, SG-ICL prompts the LLM to create its own. Here’s how it works:
- Guided generation. We give the LLM a bit of context. We provide the specific task instructions and the kind of output we expect, basically telling it what it should write.
- LLM writes the examples. LLM generates text samples that fit the task.
- Learn from within. Self-generated examples go straight back into the context, removing the need for any external data.
Results
- Improved performance. Get better results without having to find or create additional training data.
- Greater consistency. As the LLM creates the examples, performance becomes more consistent.
Could SG-ICL work for you?
This approach has great potential:
- Custom solutions. Adapting language models for specific tasks.
- Solve data scarcity issues. Particularly helpful when a large data set is not readily available.