Both COT@32 and 5-shot learning are techniques used in training large language models (LLMs), but they differ in their approach and application. Gemini models are trained with COT@32 instead of % shot Learning.
Lets explore the difference between these two techniques below.
5-shot learning:
- Definition: In 5-shot learning, the LLM is trained on only 5 examples of each specific task or concept it needs to learn. This simulates limited real-world data scenarios and encourages the model to learn by generalizing beyond the examples.
- Applications: 5-shot learning is commonly used for few-shot learning tasks, where the LLM needs to quickly adapt to new tasks with minimal data. It’s also useful for situations where large training datasets are unavailable or impractical.
- Pros: Simple to implement, computationally efficient, encourages generalization.
- Cons: Performance can be sensitive to the quality and relevance of the few-shot examples. May not be suitable for complex tasks.
COT@32 (Chain-of-Thought Prompting with 32 samples):
- Definition: COT@32 involves providing the LLM with a “chain of thought” prompt that explicitly outlines the reasoning steps needed to solve a task. The model then generates 32 different versions of the answer or solution, and the final output is chosen based on a consensus vote among these versions.
- Applications: COT@32 is primarily used to improve the performance of LLMs on complex tasks, particularly those requiring multi-step reasoning or explanation. It can also help reduce the reliance on large training datasets.
- Pros: Can improve accuracy and robustness on complex tasks, provides insights into the model’s reasoning process.
- Cons: More computationally expensive than 5-shot learning, requires careful crafting of the chain-of-thought prompts.
Key Differences:
- Data usage: 5-shot learning uses 5 examples per task, while COT@32 generates 32 versions of the answer based on a single prompt.
- Reasoning: 5-shot learning relies on implicit learning from examples, while COT@32 explicitly guides the model’s reasoning process.
- Computational cost: COT@32 is more computationally expensive due to the 32 generated versions.
- Applications: 5-shot learning is good for quick adaptation to new tasks, while COT@32 is better for complex reasoning tasks.
Which is better?
It depends on your specific needs and goals. If you need a simple and efficient approach for basic tasks, 5-shot learning might be enough. However, for complex tasks requiring multi-step reasoning and high accuracy, COT@32 could be a better choice.
Additional notes:
- Recent research suggests that COT@32 can outperform 5-shot learning on certain benchmarks, but this may not always be the case.
- It’s important to consider the trade-offs between simplicity, computational cost, and performance when choosing an approach.
I hope this explanation clarifies the differences between COT@32 and 5-shot learning and helps you make informed decisions for your LLM training needs.
