Diagnosing LLM Judge Reliability: Conformal Prediction Sets and Transitivity Violations

Assess LLM judge reliability by detecting per-input inconsistencies using transitivity analysis and quantifying output reliability with conformal prediction sets. This toolkit helps build more trustworthy and robust automated NLG evaluation systems.

advanced1-2 days5 steps

The play

Acknowledge LLM Judge Inconsistency
Understand that LLM-as-judge frameworks for NLG evaluation often suffer from per-instance reliability issues and widespread inconsistencies in their decisions.
Implement Transitivity Analysis
Apply transitivity analysis to your LLM judge's pairwise comparisons to identify and diagnose specific instances of inconsistent judgments. This reveals where your LLM judge contradicts itself.
Quantify Reliability with Conformal Prediction Sets
Integrate conformal prediction sets into your evaluation pipeline to quantify the per-instance reliability of your LLM judge's outputs, providing confidence scores for individual assessments.
Diagnose & Improve Evaluation Systems
Use the insights from transitivity violations and conformal prediction scores to identify weak points in your LLM-based evaluation system. Refine prompts, models, or data to improve reliability and trustworthiness.
Validate with Benchmarks
Apply this diagnostic toolkit to established benchmarks (e.g., SummEval) to validate improvements and ensure your LLM judge provides robust and reliable evaluations for your specific NLG tasks.

Starter code

# Example: Basic LLM Judge Setup for comparing two texts
# Note: Implementing transitivity analysis and conformal prediction sets requires more advanced statistical and ML libraries.

from openai import OpenAI

client = OpenAI()

def llm_judge_comparison(text_a: str, text_b: str) -> str:
    prompt = f"""You are an impartial judge evaluating the quality of two texts.
    Which text is better, Text A or Text B, based on clarity, coherence, and relevance?
    Respond with 'Text A is better', 'Text B is better', or 'They are equally good'.

    Text A: {text_a}
    Text B: {text_b}
    """
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.1
    )
    return response.choices[0].message.content

# Example Usage:
# result = llm_judge_comparison("The quick brown fox jumps.", "A speedy fox leaps.")
# print(result)

Source

Paperarxiv.org