BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence

Evaluate Large Language Model (LLM) confidence using a decision-theoretic framework like BAS. This approach addresses 'confident incorrectness' by enabling LLMs to abstain and accounts for varying risk preferences, leading to more reliable and trustworthy AI deployments.

intermediate30 min5 steps

The play

Acknowledge LLM Confident Incorrectness
Understand that Large Language Models frequently provide wrong answers with high certainty, posing significant risks in critical applications.
Prioritize Abstention as a Valid Outcome
Recognize that an LLM abstaining from answering a query is often safer and more preferable than generating a confidently incorrect response.
Shift LLM Evaluation Metrics
Move beyond simple accuracy metrics. Integrate sophisticated confidence assessment and risk management into your LLM development and deployment workflows, considering confidence levels and risk tolerance.
Explore Decision-Theoretic Frameworks
Investigate evaluation frameworks, such as the proposed 'BAS' method, that assess LLM performance based on how confidence informs decisions under different risk preferences.
Implement Confidence Calibration
Develop or integrate methods to fine-tune or prompt LLMs for better confidence calibration. Utilize these confidence scores for dynamic decision-making and to enable appropriate abstention.

Starter code

import random

def query_llm_with_confidence(prompt: str) -> tuple[str, float]:
    """
    Simulates an LLM query returning an answer and a confidence score.
    In a real scenario, this would involve a calibrated LLM API call or an agent that extracts confidence.
    """
    # Placeholder logic: real LLM would generate this
    if "capital of france" in prompt.lower():
        return "Paris", 0.98
    elif "square root of -4" in prompt.lower():
        return "Undefined", 0.95 # LLM correctly abstains or states undefined
    else:
        answer = f"The answer to '{prompt}' is a simulated response."
        confidence = round(random.uniform(0.5, 0.99), 2)
        return answer, confidence

# Example usage for a confidence-aware LLM interaction
prompt1 = "What is the capital of France?"
answer1, conf1 = query_llm_with_confidence(prompt1)
print(f"Prompt: '{prompt1}'\nAnswer: '{answer1}', Confidence: {conf1}")

prompt2 = "Who won the World Series in 1900?" # A more obscure question
answer2, conf2 = query_llm_with_confidence(prompt2)
print(f"Prompt: '{prompt2}'\nAnswer: '{answer2}', Confidence: {conf2}")

prompt3 = "Calculate 10 / 0."
answer3, conf3 = query_llm_with_confidence(prompt3)
print(f"Prompt: '{prompt3}'\nAnswer: '{answer3}', Confidence: {conf3}")

Source

Paperarxiv.org