Dataset Quality Validation for LLM Fine-Tuning

Dataset Quality Validation for LLM Fine-tuning

Dataset validation is a mandatory step before launching costly fine-tuning. Poor datasets are only discovered after training, when GPU hours and time are spent. Systematic validation prevents this.

Validation Levels

Level 1 — Technical (automated):

from dataclasses import dataclass
import pandas as pd

@dataclass
class ValidationReport:
    total_examples: int
    issues: dict
    pass_rate: float
    recommendations: list[str]

class DatasetValidator:
    def validate(self, dataset: list[dict]) -> ValidationReport:
        issues = {
            'empty_outputs': [],
            'too_short': [],
            'too_long': [],
            'truncated': [],
            'encoding_issues': [],
            'near_duplicates': [],
        }

        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

        for i, ex in enumerate(dataset):
            output = ex.get('output', '')

            # Empty outputs
            if not output.strip():
                issues['empty_outputs'].append(i)
                continue

            # Length in tokens
            tokens = tokenizer.encode(output)
            if len(tokens) < 5:
                issues['too_short'].append(i)
            elif len(tokens) > 2000:
                issues['too_long'].append(i)

            # Potentially truncated text (ends without complete phrase)
            if output.strip()[-1] not in '.!?])"\'':
                if len(tokens) > 500:  # Long text without ending
                    issues['truncated'].append(i)

            # Encoding issues
            try:
                output.encode('utf-8').decode('utf-8')
            except (UnicodeEncodeError, UnicodeDecodeError):
                issues['encoding_issues'].append(i)

        total_issues = sum(len(v) for v in issues.values())
        pass_rate = 1 - total_issues / len(dataset)

        return ValidationReport(
            total_examples=len(dataset),
            issues=issues,
            pass_rate=pass_rate,
            recommendations=self._generate_recommendations(issues, len(dataset))
        )

Level 2 — Semantic (automated):

class SemanticValidator:
    def check_instruction_output_alignment(self, dataset: list[dict],
                                            sample_size: int = 200) -> float:
        """How well output addresses instruction"""
        sample = random.sample(dataset, min(sample_size, len(dataset)))

        alignment_scores = []
        for ex in sample:
            score = self._compute_alignment(
                ex['instruction'], ex.get('input', ''), ex['output']
            )
            alignment_scores.append(score)

        return np.mean(alignment_scores)

    def _compute_alignment(self, instruction: str, input: str, output: str) -> float:
        """LLM-judge for relevance assessment"""
        prompt = f"""Does this output correctly address the instruction?

Instruction: {instruction}
Input: {input}
Output: {output[:500]}

Rate relevance 1-5, return only number."""

        response = llm_client.complete(prompt, max_tokens=5)
        try:
            score = int(response.strip()) / 5.0
        except ValueError:
            score = 0.5  # Uncertainty → average score

        return score

Level 3 — Substantive (manual review):

def sample_for_human_review(dataset: list[dict],
                              n: int = 100) -> list[dict]:
    """Stratified sample for manual review"""
    # By output length
    short = [ex for ex in dataset if len(ex['output'].split()) < 50]
    medium = [ex for ex in dataset if 50 <= len(ex['output'].split()) < 200]
    long = [ex for ex in dataset if len(ex['output'].split()) >= 200]

    sample = []
    per_stratum = n // 3
    for stratum in [short, medium, long]:
        sample.extend(random.sample(stratum, min(per_stratum, len(stratum))))

    return sample

Final Report Before Training

def generate_pre_training_report(dataset: list[dict]) -> str:
    validator = DatasetValidator()
    semantic_val = SemanticValidator()

    tech_report = validator.validate(dataset)
    alignment_score = semantic_val.check_instruction_output_alignment(dataset)

    report = f"""
## Dataset Validation Report

**Total examples:** {tech_report.total_examples:,}
**Technical pass rate:** {tech_report.pass_rate:.1%}
**Instruction-Output alignment:** {alignment_score:.2f}/1.0

### Issues Found:
- Empty outputs: {len(tech_report.issues['empty_outputs'])}
- Too short (<5 tokens): {len(tech_report.issues['too_short'])}
- Too long (>2000 tokens): {len(tech_report.issues['too_long'])}
- Potentially truncated: {len(tech_report.issues['truncated'])}
- Near-duplicates: {len(tech_report.issues['near_duplicates'])}

### Recommendations:
{chr(10).join('- ' + r for r in tech_report.recommendations)}

**GO / NO-GO:** {'GO' if tech_report.pass_rate > 0.9 and alignment_score > 0.7 else 'NO-GO — fix issues before training'}
"""
    return report

Go/no-go threshold: technical pass rate > 90%, alignment score > 0.70. With alignment < 0.70 — dataset contains examples where output does not answer instruction, which actively degrades the model.