autoregenerative_feedback.py

import pandas as pd
import numpy as np

def load_evaluation_summary(file_path="evaluation_summary.csv"):
    """Loads a summary of evaluation results."""
    try:
        return pd.read_csv(file_path)
    except FileNotFoundError:
        print(f"Error: Evaluation summary file not found at {file_path}")
        return pd.DataFrame()

def provide_autoregenerative_feedback(evaluation_summary_df):
    """Provides conceptual autoregenerative feedback based on evaluation summary.
    In a real system, this would involve analyzing trends and specific metrics
    to suggest concrete adjustments to the model (e.g., fine-tuning parameters,
    data augmentation strategies, prompt engineering adjustments).
    """
    print("\n--- Autoregenerative Feedback System ---")

    if evaluation_summary_df.empty:
        print("No evaluation summary available to provide feedback.")
        return {}

    feedback_suggestions = {}

    # Example: Analyze overall alignment score from psychometrics
    if "overall_alignment" in evaluation_summary_df.columns:
        avg_alignment = evaluation_summary_df["overall_alignment"].mean()
        print(f"Average Human-AI Alignment: {avg_alignment:.2f}")
        if avg_alignment < 0.7:
            feedback_suggestions["alignment"] = "Consider refining model's value alignment or persona to improve human-AI integration. Focus on training data that reflects desired ethical and social norms."
        else:
            feedback_suggestions["alignment"] = "Human-AI alignment is generally good. Continue monitoring and fine-tuning for subtle improvements."

    # Example: Analyze micro-level metrics (e.g., lexical richness)
    if "lexical_richness_ttr" in evaluation_summary_df.columns:
        avg_lexical_richness = evaluation_summary_df["lexical_richness_ttr"].mean()
        print(f"Average Lexical Richness (TTR): {avg_lexical_richness:.2f}")
        if avg_lexical_richness < 0.6:
            feedback_suggestions["lexical_richness"] = "Model outputs show low lexical richness. Explore diverse training data or adjust decoding strategies (e.g., temperature, top-k sampling) to encourage more varied vocabulary."
        else:
            feedback_suggestions["lexical_richness"] = "Lexical richness is adequate. Maintain diversity in generated text."

    # Example: Analyze macro-level sentiment polarity
    if "sentiment_polarity" in evaluation_summary_df.columns:
        avg_sentiment_polarity = evaluation_summary_df["sentiment_polarity"].mean()
        print(f"Average Sentiment Polarity: {avg_sentiment_polarity:.2f}")
        if avg_sentiment_polarity < -0.1:
            feedback_suggestions["sentiment_bias"] = "Model outputs show a negative sentiment bias. Review training data for potential biases or implement sentiment-aware fine-tuning."
        elif avg_sentiment_polarity > 0.1:
            feedback_suggestions["sentiment_bias"] = "Model outputs show a positive sentiment bias. Ensure neutrality where appropriate."
        else:
            feedback_suggestions["sentiment_bias"] = "Sentiment polarity is balanced. Good."

    # Example: Analyze benchmark results (e.g., BBQ bias score)
    if "bbq_bias_score_race" in evaluation_summary_df.columns:
        avg_bbq_bias = evaluation_summary_df["bbq_bias_score_race"].mean()
        print(f"Average BBQ Bias Score (Race): {avg_bbq_bias:.2f}")
        if avg_bbq_bias > 0.2:
            feedback_suggestions["bias_mitigation"] = "High bias detected in BBQ benchmark. Prioritize bias mitigation techniques, such as debiasing datasets or adversarial training."
        else:
            feedback_suggestions["bias_mitigation"] = "Bias scores are acceptable. Continue monitoring."

    print("\nFeedback Suggestions:")
    for key, value in feedback_suggestions.items():
        print(f"  - {key.replace('_', ' ').title()}: {value}")

    return feedback_suggestions

if __name__ == "__main__":
    # Dummy evaluation summary (in a real scenario, this would be a consolidated result
    # from all previous evaluation steps)
    dummy_evaluation_summary = pd.DataFrame({
        "overall_alignment": [0.65, 0.72, 0.68],
        "lexical_richness_ttr": [0.55, 0.62, 0.58],
        "sentiment_polarity": [-0.05, 0.02, -0.01],
        "bbq_bias_score_race": [0.25, 0.18, 0.30],
        "factual_accuracy": [0.8, 0.85, 0.79]
    })

    feedback = provide_autoregenerative_feedback(dummy_evaluation_summary)