REAL_DATA_FETCH_STATUS.md

Real Benchmark Data Fetch - In Progress

Status: ⏳ RUNNING
Started: Now
ETA: 10-15 minutes

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🎯 What's Happening

We're fetching REAL per-question success rates from the top 5 models on the OpenLLM Leaderboard for MMLU.

Models Being Queried

1. meta-llama/Meta-Llama-3.1-70B-Instruct (~85% MMLU)
2. Qwen/Qwen2.5-72B-Instruct (~85% MMLU)
3. mistralai/Mixtral-8x22B-Instruct-v0.1 (~77% MMLU)
4. google/gemma-2-27b-it (~75% MMLU)
5. microsoft/Phi-3-medium-128k-instruct (~78% MMLU)

Data Being Collected

• 14,042 MMLU questions per model
• Per-question correctness (0 or 1)
• Aggregated success rate across all 5 models
• Difficulty classification based on real performance

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📊 What We'll Get

Per-Question Data

{
  "mmlu_42": {
    "question_text": "Statement 1 | Some abelian group...",
    "success_rate": 0.60,  // 3 out of 5 models got it right
    "num_models_tested": 5,
    "difficulty_tier": "medium",
    "difficulty_label": "Moderate",
    "model_results": {
      "meta-llama__Meta-Llama-3.1-70B-Instruct": 1,
      "Qwen__Qwen2.5-72B-Instruct": 1,
      "mistralai__Mixtral-8x22B-Instruct-v0.1": 0,
      "google__gemma-2-27b-it": 1,
      "microsoft__Phi-3-medium-128k-instruct": 0
    }
  }
}

Expected Distribution

Based on top model performance:

• LOW success (0-30%): ~10-15% of questions (hard for even best models)
• MEDIUM success (30-70%): ~25-35% of questions (capability boundary)
• HIGH success (70-100%): ~50-65% of questions (mastered)

This gives us the full spectrum to understand LLM capability boundaries!

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🔍 Why This Approach is Better

What We Tried First

❌ Domain-level estimates: All questions in a domain get same score
❌ Manual evaluation: Too slow, expensive
❌ Clustering: Groups questions but doesn't give individual scores

What We're Doing Now ✅

Real per-question success rates from top models

Advantages:

1. Granular: Each question has its own difficulty score
2. Accurate: Based on actual model performance
3. Current: Uses latest top models
4. Explainable: "5 top models got this right" vs "estimated 45%"

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⏱️ Timeline

Step	Status	Time
Fetch Model 1 (Llama 3.1 70B)	⏳ Running	~3 min
Fetch Model 2 (Qwen 2.5 72B)	⏳ Queued	~3 min
Fetch Model 3 (Mixtral 8x22B)	⏳ Queued	~3 min
Fetch Model 4 (Gemma 2 27B)	⏳ Queued	~3 min
Fetch Model 5 (Phi-3 Medium)	⏳ Queued	~3 min
Aggregate Success Rates	⏳ Pending	~1 min
Save Results	⏳ Pending	<1 min

Total: ~10-15 minutes

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📦 Output Files

Main Output

./data/benchmark_results/mmlu_real_results.json

Contains:

• Metadata (models, fetch time, counts)
• Questions with real success rates
• Difficulty classifications

Statistics

• Total questions collected
• Difficulty tier distribution
• Success rate statistics (min, max, mean, median)

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🚀 Next Steps (After Fetch Completes)

Immediate

1. ✅ Review fetched data quality
2. ✅ Verify difficulty distribution makes sense
3. ✅ Check for any data issues

Then

1. Load into vector DB: Use real success rates
2. Build embeddings: Generate for all questions
3. Test queries: "Calculate quantum corrections..." → find similar hard questions
4. Validate accuracy: Does it correctly identify hard vs easy prompts?

Finally

1. Integrate with MCP: togmal_check_prompt_difficulty uses real data
2. Deploy to production: Ready for use in Claude Desktop
3. Monitor performance: Track query speed, accuracy

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💡 Key Innovation

We're not estimating difficulty - we're measuring it directly from the world's best models.

This means:

• ✅ No guesswork: Real performance data
• ✅ Cross-model consensus: 5 top models agree/disagree
• ✅ Capability boundary detection: Find questions at 30-50% success (most interesting!)
• ✅ Actionable insights: "Similar to questions that 4/5 top models fail"

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📈 Expected Results

Difficulty Tiers

Based on top model performance patterns:

LOW Success (0-30%) - ~500-1000 questions

• Graduate-level reasoning
• Multi-step problem solving
• Domain-specific expertise
• These are the gold mine for detecting LLM limits!

MEDIUM Success (30-70%) - ~2000-3000 questions

• Capability boundary
• Requires careful reasoning
• Some models succeed, others fail
• Most interesting for adaptive prompting

HIGH Success (70-100%) - ~8000-10000 questions

• Within LLM capability
• Baseline knowledge
• Factual recall
• Good for validation

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🎯 Success Metrics

Data Quality

• All 5 models fetched successfully
• 1000+ questions with complete data
• Difficulty distribution looks reasonable
• No major data anomalies

Performance

• Fetch completes in <20 minutes
• All questions have success rates
• Stratification works (low/medium/high)
• JSON file validates

Usability

• Data format ready for vector DB
• Metadata preserved (domains, questions)
• Can be post-processed easily
• Documented and reproducible

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Current Status: Script running, check back in ~15 minutes!

Run this to check progress:

tail -f <terminal_output>

Or check the output file:

ls -lh ./data/benchmark_results/mmlu_real_results.json