Implementing iterative refinement
Iterative refinement is a process where a model’s response is progressively improved through repeated cycles of self-evaluation and revision. Each cycle uses a reflection prompt to guide the model in critiquing and enhancing its prior output, aiming to converge on a more accurate or well-articulated result.
To implement iterative refinement, we can create a loop that repeatedly applies the reflection process. Here’s an example:
- Define the
iterative_Reflection
function:from transformers import AutoModelForCausalLM, AutoTokenizer def iterative_Reflection( model, tokenizer, task, max_iterations=3 ): response = generate_initial_response(model, tokenizer, task) for i in range(max_iterations): prompt = Reflection_prompt(task, response) inputs = tokenizer(prompt, return_tensors...