Bias Mitigation Techniques for LLM Evaluation

This reference details specific techniques for mitigating known biases in LLM-as-a-Judge systems.

Position Bias

The Problem

In pairwise comparison, LLMs systematically prefer responses in certain positions. Research shows:

• GPT has mild first-position bias (~55% preference for first position in ties)
• Claude shows similar patterns
• Smaller models often show stronger bias

Mitigation: Position Swapping Protocol

async def position_swap_comparison(response_a, response_b, prompt, criteria):
    # Pass 1: Original order
    result_ab = await compare(response_a, response_b, prompt, criteria)
    
    # Pass 2: Swapped order
    result_ba = await compare(response_b, response_a, prompt, criteria)
    
    # Map second result (A in second position → B in first)
    result_ba_mapped = {
        'winner': {'A': 'B', 'B': 'A', 'TIE': 'TIE'}[result_ba['winner']],
        'confidence': result_ba['confidence']
    }
    
    # Consistency check
    if result_ab['winner'] == result_ba_mapped['winner']:
        return {
            'winner': result_ab['winner'],
            'confidence': (result_ab['confidence'] + result_ba_mapped['confidence']) / 2,
            'position_consistent': True
        }
    else:
        # Disagreement indicates position bias was a factor
        return {
            'winner': 'TIE',
            'confidence': 0.5,
            'position_consistent': False,
            'bias_detected': True
        }

Alternative: Multiple Shuffles

For higher reliability, use multiple position orderings:

async def multi_shuffle_comparison(response_a, response_b, prompt, criteria, n_shuffles=3):
    results = []
    for i in range(n_shuffles):
        if i % 2 == 0:
            r = await compare(response_a, response_b, prompt, criteria)
        else:
            r = await compare(response_b, response_a, prompt, criteria)
            r['winner'] = {'A': 'B', 'B': 'A', 'TIE': 'TIE'}[r['winner']]
        results.append(r)
    
    # Majority vote
    winners = [r['winner'] for r in results]
    final_winner = max(set(winners), key=winners.count)
    agreement = winners.count(final_winner) / len(winners)
    
    return {
        'winner': final_winner,
        'confidence': agreement,
        'n_shuffles': n_shuffles
    }

Length Bias

The Problem

LLMs tend to rate longer responses higher, regardless of quality. This manifests as:

• Verbose responses receiving inflated scores
• Concise but complete responses penalized
• Padding and repetition being rewarded

Mitigation: Explicit Prompting

Include anti-length-bias instructions in the prompt:

CRITICAL EVALUATION GUIDELINES:
- Do NOT prefer responses because they are longer
- Concise, complete answers are as valuable as detailed ones
- Penalize unnecessary verbosity or repetition
- Focus on information density, not word count

Mitigation: Length-Normalized Scoring

def length_normalized_score(score, response_length, target_length=500):
    """Adjust score based on response length."""
    length_ratio = response_length / target_length
    
    if length_ratio > 2.0:
        # Penalize excessively long responses
        penalty = (length_ratio - 2.0) * 0.1
        return max(score - penalty, 1)
    elif length_ratio < 0.3:
        # Penalize excessively short responses
        penalty = (0.3 - length_ratio) * 0.5
        return max(score - penalty, 1)
    else:
        return score

Mitigation: Separate Length Criterion

Make length a separate, explicit criterion so it's not implicitly rewarded:

criteria = [
    {"name": "Accuracy", "description": "Factual correctness", "weight": 0.4},
    {"name": "Completeness", "description": "Covers key points", "weight": 0.3},
    {"name": "Conciseness", "description": "No unnecessary content", "weight": 0.3}  # Explicit
]

Self-Enhancement Bias

The Problem

Models rate outputs generated by themselves (or similar models) higher than outputs from different models.

Mitigation: Cross-Model Evaluation

Use a different model family for evaluation than generation:

def get_evaluator_model(generator_model):
    """Select evaluator to avoid self-enhancement bias."""
    if 'gpt' in generator_model.lower():
        return 'claude-4-5-sonnet'
    elif 'claude' in generator_model.lower():
        return 'gpt-5.2'
    else:
        return 'gpt-5.2'  # Default

Mitigation: Blind Evaluation

Remove model attribution from responses before evaluation:

def anonymize_response(response, model_name):
    """Remove model-identifying patterns."""
    patterns = [
        f"As {model_name}",
        "I am an AI",
        "I don't have personal opinions",
        # Model-specific patterns
    ]
    anonymized = response
    for pattern in patterns:
        anonymized = anonymized.replace(pattern, "[REDACTED]")
    return anonymized

Verbosity Bias

The Problem

Detailed explanations receive higher scores even when the extra detail is irrelevant or incorrect.

Mitigation: Relevance-Weighted Scoring

async def relevance_weighted_evaluation(response, prompt, criteria):
    # First, assess relevance of each segment
    relevance_scores = await assess_relevance(response, prompt)
    
    # Weight evaluation by relevance
    segments = split_into_segments(response)
    weighted_scores = []
    for segment, relevance in zip(segments, relevance_scores):
        if relevance > 0.5:  # Only count relevant segments
            score = await evaluate_segment(segment, prompt, criteria)
            weighted_scores.append(score * relevance)
    
    return sum(weighted_scores) / len(weighted_scores)

Mitigation: Rubric with Verbosity Penalty

Include explicit verbosity penalties in rubrics:

rubric_levels = [
    {
        "score": 5,
        "description": "Complete and concise. All necessary information, nothing extraneous.",
        "characteristics": ["Every sentence adds value", "No repetition", "Appropriately scoped"]
    },
    {
        "score": 3,
        "description": "Complete but verbose. Contains unnecessary detail or repetition.",
        "characteristics": ["Main points covered", "Some tangents", "Could be more concise"]
    },
    # ... etc
]

Authority Bias

The Problem

Confident, authoritative tone is rated higher regardless of accuracy.

Mitigation: Evidence Requirement

Require explicit evidence for claims:

For each claim in the response:
1. Identify whether it's a factual claim
2. Note if evidence or sources are provided
3. Score based on verifiability, not confidence

IMPORTANT: Confident claims without evidence should NOT receive higher scores than 
hedged claims with evidence.

Mitigation: Fact-Checking Layer

Add a fact-checking step before scoring:

async def fact_checked_evaluation(response, prompt, criteria):
    # Extract claims
    claims = await extract_claims(response)
    
    # Fact-check each claim
    fact_check_results = await asyncio.gather(*[
        verify_claim(claim) for claim in claims
    ])
    
    # Adjust score based on fact-check results
    accuracy_factor = sum(r['verified'] for r in fact_check_results) / len(fact_check_results)
    
    base_score = await evaluate(response, prompt, criteria)
    return base_score * (0.7 + 0.3 * accuracy_factor)  # At least 70% of score

Aggregate Bias Detection

Monitor for systematic biases in production:

class BiasMonitor:
    def __init__(self):
        self.evaluations = []
    
    def record(self, evaluation):
        self.evaluations.append(evaluation)
    
    def detect_position_bias(self):
        """Detect if first position wins more often than expected."""
        first_wins = sum(1 for e in self.evaluations if e['first_position_winner'])
        expected = len(self.evaluations) * 0.5
        z_score = (first_wins - expected) / (expected * 0.5) ** 0.5
        return {'bias_detected': abs(z_score) > 2, 'z_score': z_score}
    
    def detect_length_bias(self):
        """Detect if longer responses score higher."""
        from scipy.stats import spearmanr
        lengths = [e['response_length'] for e in self.evaluations]
        scores = [e['score'] for e in self.evaluations]
        corr, p_value = spearmanr(lengths, scores)
        return {'bias_detected': corr > 0.3 and p_value < 0.05, 'correlation': corr}

Summary Table

Bias	Primary Mitigation	Secondary Mitigation	Detection Method
Position	Position swapping	Multiple shuffles	Consistency check
Length	Explicit prompting	Length normalization	Length-score correlation
Self-enhancement	Cross-model evaluation	Anonymization	Model comparison study
Verbosity	Relevance weighting	Rubric penalties	Relevance scoring
Authority	Evidence requirement	Fact-checking layer	Confidence-accuracy correlation