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llm-instruction-following-code

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instruction-following
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LLM Instruction-Following Evaluation Framework - Code Repository

Paper Dataset Python License

This repository contains the complete evaluation framework used in our paper "When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs" (arXiv:2510.18892).

📋 What's Included

This code repository provides everything needed to:

  • ✅ Reproduce our evaluation of 256 models across 20 diagnostic tests
  • ✅ Run the evaluation on new models
  • ✅ Add your own custom instruction-following tests
  • ✅ Generate publication-quality visualizations
  • ✅ Export results to multiple formats (Excel, JSON, LaTeX)

🚀 Quick Start

Installation 

# Clone the repository or download files
pip install pandas openpyxl requests matplotlib seaborn numpy

# Set your OpenRouter API key
export OPENROUTER_API_KEY="your_api_key_here"

Run Evaluation 

# Run comprehensive evaluation (256 models × 20 tests)
python test_comprehensive_20_verified.py

# Generate analysis and visualizations
python analyze_comprehensive_final.py

📁 Key Files

Core Evaluation

  • test_comprehensive_20_verified.py - Main test runner

    • Evaluates models across all 20 diagnostic tests
    • Exact-match evaluation with normalized whitespace
    • Exports results to Excel with multiple sheets
    • ~6-8 hours for full 256-model evaluation

  • questions.json - Complete test bank (20 diagnostic prompts)

    • Each test includes: prompt, expected output, category, difficulty
    • Covers 5 categories: String Manipulation, Constraint Compliance, Text Processing, Structured Data, Complex Operations
    • Frozen version used for paper evaluation

  • models_verified_working_v2_20251014_091649.py - Model configuration

    • 256 verified working models from OpenRouter
    • Pre-verified for basic functionality
    • Includes provider information

Analysis & Visualization

  • analyze_comprehensive_final.py - Comprehensive analysis pipeline
    • Generates 4 publication-quality PDF figures
    • Creates LaTeX tables for paper integration
    • Computes statistical summaries
    • Category and provider performance breakdowns

Supporting Files

  • requirements.txt - Python dependencies
  • README.md - This file (setup and usage instructions)

🧪 Test Categories

Our 20 diagnostic tests cover five categories:

1. String Manipulation (Tests 1, 3, 5, 17, 20) - HARDEST

  • Multi-step text transformations
  • Average pass rate: 12.0%
  • Example: Test 5 (Complex String Transformation) - only 2.7% pass rate

2. Constraint Compliance (Tests 2, 9, 15) - EASIEST

  • Following exact output specifications
  • Average pass rate: 66.9%
  • Example: Test 2 (Exact Output Compliance) - 96.1% pass rate

3. Text Processing (Test 13)

  • Targeted text manipulation tasks
  • Average pass rate: 50.5%

4. Structured Data (Tests 4, 6, 10, 12, 14)

  • JSON, Markdown, CSV generation
  • Average pass rate: 41.1%

5. Complex Operations (Tests 7, 8, 11, 16, 18, 19)

  • Multi-step reasoning and computation
  • Average pass rate: 35.0%

📊 Evaluation Methodology

Exact Match Evaluation

  • Binary Pass/Fail: No partial credit
  • Whitespace Normalized: Leading/trailing spaces ignored
  • Case Sensitive: Preserves intentional capitalization
  • Format Strict: JSON, tables, special characters must be exact

Why Exact Match?

  1. Objectivity - Eliminates subjective judgment
  2. Reproducibility - Deterministic, verifiable results
  3. Clarity - Binary success/failure (no ambiguity)
  4. Efficiency - No manual review needed
  5. Diagnostic Power - Reveals specific failure modes

📈 Results Summary

From our October 14, 2025 evaluation of 256 models:

  • Overall Pass Rate: 43.7%
  • Best Model: qwen/qwen-plus-2025-07-28:thinking (100%)
  • Most Difficult Test: Test 5 - Complex String Transformation (2.7%)
  • Top Provider: x-ai (79.3% average across 15 models)

🔧 Customization

Adding New Tests

Edit questions.json to add new diagnostic tests:

{
  "id": 21,
  "test_name": "Your New Test",
  "category": "Custom Category",
  "difficulty": "medium",
  "prompt": "Your instruction prompt here",
  "expected_output": "Exact expected response",
  "exact_match": true,
  "case_sensitive": false
}

Testing Custom Models

Modify models_verified_working_v2_20251014_091649.py or create your own model list:

MODELS = [
    {
        "name": "provider/model-name",
        "provider": "provider",
        "verified": True
    },
    # Add more models...
]

Adjusting Analysis

Customize analyze_comprehensive_final.py to:

  • Change visualization styles
  • Add new analysis metrics
  • Modify export formats
  • Create custom reports

📦 Output Files

The evaluation produces:

  1. Excel Workbook (comprehensive_20_tests_results_YYYYMMDD_HHMMSS.xlsx)

    • Overview sheet with summary statistics
    • Model rankings (sorted by performance)
    • Test difficulty analysis
    • Category performance breakdown
    • Complete raw results (all 5,120 evaluations)
    • Test descriptions

  2. JSON Export (comprehensive_20_tests_results_YYYYMMDD_HHMMSS.json)

    • Machine-readable format
    • Includes metadata and timestamps
    • All test results with responses

  3. PDF Visualizations

    • fig1_heatmap.pdf - Performance matrix
    • fig2_provider.pdf - Provider comparison
    • fig3_difficulty.pdf - Test difficulty
    • fig4_category.pdf - Category performance

  4. LaTeX Tables (paper_tables.tex)

    • Ready for paper integration
    • Formatted with booktabs package

🔍 Reproducibility

To exactly reproduce our paper results:

# Use the frozen model list from October 14, 2025
python test_comprehensive_20_verified.py

# Use the frozen test bank
# (questions.json is already frozen at 20 tests)

# Generate analysis with same parameters
python analyze_comprehensive_final.py

Note: Model outputs may vary over time as providers update their models. For exact reproducibility, use the snapshot from our evaluation date.

💡 Usage Examples

Quick Test (5 models) 

# Edit test_comprehensive_20_verified.py
# Change MODELS to a subset:
MODELS = [
    "openai/gpt-4o",
    "anthropic/claude-3.7-sonnet",
    "google/gemini-2.0-flash-exp:free",
    "meta-llama/llama-3.3-70b-instruct",
    "qwen/qwen-plus-2025-07-28:thinking"
]

Single Model Test 

import requests
import json

# Load questions
with open('questions.json', 'r') as f:
    questions = json.load(f)

# Test a single model
model = "openai/gpt-4o"
for q in questions:
    response = requests.post(
        "https://openrouter.ai/api/v1/chat/completions",
        headers={"Authorization": f"Bearer {OPENROUTER_API_KEY}"},
        json={
            "model": model,
            "messages": [{"role": "user", "content": q["prompt"]}]
        }
    )
    # Evaluate response...

Custom Analysis 

import pandas as pd

# Load results
df = pd.read_excel('results.xlsx', sheet_name='All Results')

# Custom analysis
top_models = df.groupby('model')['passed'].mean().sort_values(ascending=False).head(10)
print(top_models)

# Category performance
category_perf = df.groupby('category')['passed'].mean()
print(category_perf)

🐛 Troubleshooting

Common Issues

1. API Rate Limiting

# OpenRouter may rate limit. Add delays between requests:
time.sleep(1)  # Add to test_comprehensive_20_verified.py

2. JSON Serialization Errors

# Use export_json_from_excel.py to convert numpy types
python export_json_from_excel.py

3. Missing Packages

pip install pandas openpyxl requests matplotlib seaborn numpy

4. API Key Not Set

export OPENROUTER_API_KEY="your_key_here"
# Or set in Python: os.environ['OPENROUTER_API_KEY'] = "your_key"

📚 Citation

If you use this code in your research, please cite:

@article{young2025instruction,
  title={When Models Can't Follow: Testing Instruction Adherence Across 256 LLMs},
  author={Young, Richard J. and Gillins, Brandon and Matthews, Alice M.},
  journal={arXiv preprint arXiv:2510.18892},
  year={2025}
}

🔗 Related Resources

📞 Contact

Research Team:

Affiliation: University of Nevada, Las Vegas

🙏 Acknowledgments

  • OpenRouter for unified API access to 256+ models
  • Model Providers (OpenAI, Anthropic, Google, Meta, Qwen, DeepSeek, x-ai, and others)
  • Open source community for evaluation tools and frameworks

📜 License

This code is released under the MIT License.

MIT License

Copyright (c) 2025 Richard J. Young, Brandon Gillins, Alice M. Matthews

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Repository Version: 1.0 Last Updated: October 23, 2025 Evaluation Date: October 14, 2025

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