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Reflection

Description

Reflection in LLMs refers to a model's ability to analyze, evaluate, and improve its own outputs. This meta-cognitive capability allows LLMs to engage in iterative refinement, potentially leading to higher- quality results and more robust performance.

There are several key aspects of reflection:

  • Self-evaluation of outputs
  • Identification of weaknesses or errors
  • Generation of improvement strategies
  • Iterative refinement of responses

Varieties

Self-evaluation involves the model critically assessing its own output to identify strengths, weaknesses, and areas for improvement before suggesting enhancements.

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.

Error correction focuses on identifying and fixing specific mistakes in the model's output, often guided by known errors or external feedback.

Example

def self_evaluation_prompt(task, initial_response):
    prompt = f"""Task: {task}

    Initial Response:
    {initial_response}

    Now, let's engage in self-reflection:

    1. Evaluate the strengths and weaknesses of your initial response.
    2. Identify any errors, inconsistencies, or areas for improvement.
    3. Suggest specific ways to enhance the response.
    4. Provide a revised and improved version of the response.

    Your self-reflection and improved response:
    """
    return prompt

task = "Explain the concept of quantum entanglement to a high school student."
initial_response = "Quantum entanglement is when two particles are connected in a way that measuring one instantly affects the other, no matter how far apart they are."
prompt = self_evaluation_prompt(task, initial_response)
print(prompt)

def iterative_reflection(model, tokenizer, task, max_iterations=3):
    response = generate_initial_response(model, tokenizer, task)

    for _ in range(max_iterations):
        prompt = self_evaluation_prompt(task, response)
        inputs = tokenizer(prompt, return_tensors="pt")
        outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
        reflection = tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Extract the improved response from the reflection
        response = extract_improved_response(reflection)

        if is_satisfactory(response):
            break

    return response

def generate_initial_response(model, tokenizer, task):
    prompt = f"Task: {task}\n\nResponse:"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=500, num_return_sequences=1)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

def extract_improved_response(reflection):
    # Implement logic to extract the improved response from the reflection
    # This could involve text parsing or using markers in the generated text
    pass

def is_satisfactory(response):
    # Implement logic to determine if the response meets quality criteria
    # This could involve length checks, keyword presence, or more advanced metrics
    pass

def error_correction_reflection(model, tokenizer, task, initial_response, known_errors):
    prompt = f"""Task: {task}

    Initial Response:
    {initial_response}

    Known Errors:
    {' '.join(f'- {error}' for error in known_errors)}

    Please reflect on the initial response, focusing on correcting the known errors.
    Provide an improved version of the response that addresses these issues.

    Corrected Response:
    """

    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
    corrected_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return corrected_response

task = "Describe the structure of an atom."
initial_response = "An atom consists of a nucleus containing protons and neutrons, with electrons orbiting around it in fixed circular orbits."
known_errors = [
    "Electrons do not orbit in fixed circular paths",
    "The description doesn't mention electron shells or energy levels"
]

corrected_response = error_correction_reflection(model, tokenizer, task, initial_response, known_errors)
print(corrected_response)