LLM Evaluation with W&B Sweeps

A repository demonstrating systematic LLM evaluation strategies using Weights & Biases

LLM Evaluation with Weights & Biases Sweeps

GitHub Repository

Project Overview

This project demonstrates systematic approaches to evaluating Large Language Models (LLMs) using Weights & Biases Sweeps. It provides practical examples of how to set up comprehensive evaluation pipelines for different types of LLM tasks.

Key Features

1. QA Evaluation: Framework for evaluating question-answering performance
2. Mathematical Reasoning: Specialized evaluation for mathematical problem solving
3. Parameter Optimization: Using W&B Sweeps to find optimal prompting strategies
4. Visualization: Rich visual analytics for interpreting results

Implementation Details

The repository implements several evaluation strategies:

LLM-as-Judge Evaluation

One of the most powerful approaches is using another LLM (typically a more capable one) to evaluate the responses of the target LLM:

def evaluate_with_llm_judge(question, reference_answer, model_response):
    """
    Uses a judge LLM to evaluate the quality of a model response
    compared to a reference answer.
    """
    judge_prompt = f"""
    Question: {question}
    Reference Answer: {reference_answer}
    Model Response: {model_response}
    
    Evaluate the model response on the following criteria on a scale of 1-10:
    1. Accuracy: How factually correct is the response?
    2. Completeness: How complete is the response?
    3. Relevance: How relevant is the response to the question?
    
    For each criterion, provide a score and a brief explanation.
    Finally, provide an overall score.
    """
    
    # Call the judge LLM
    evaluation = judge_llm(judge_prompt)
    
    # Parse the scores from the evaluation
    # Implementation depends on the structure of the judge's response
    
    return parsed_scores

Sweep Configuration

The project uses W&B Sweeps to systematically explore different parameters:

sweep_config = {
    "method": "grid",
    "metric": {"name": "average_accuracy", "goal": "maximize"},
    "parameters": {
        "temperature": {"values": [0.0, 0.3, 0.7, 1.0]},
        "prompt_strategy": {"values": ["direct", "cot", "few_shot"]},
        "max_tokens": {"values": [100, 200, 500]},
    }
}

This allows exploration of how different combinations of temperature, prompting strategies, and token limits affect the model’s performance.

Results and Insights

The repository includes visualizations of sweep results, demonstrating how different parameters affect various performance metrics:

• Temperature Impact: Lower temperatures generally improved factual accuracy but sometimes at the cost of completeness
• Prompt Strategies: Chain-of-Thought prompting significantly improved mathematical reasoning tasks
• Parameter Interaction: Complex interactions between parameters highlighted the importance of systematic sweeps rather than one-at-a-time optimization

Practical Applications

This evaluation framework can be applied to:

1. Model Selection: Comparing different LLMs for specific use cases
2. Prompt Engineering: Finding optimal prompting strategies
3. Fine-tuning Decisions: Determining whether fine-tuning would benefit specific tasks
4. Application Development: Building more robust LLM-powered applications

Future Work

Ongoing and planned improvements include:

• Adding support for more diverse evaluation tasks
• Implementing automated evaluation for creative and open-ended generation
• Integrating human feedback into the evaluation pipeline
• Exploring adaptive evaluation strategies that adjust to model strengths and weaknesses

Get Started

To use this framework for your own LLM evaluation:

1. Clone the repository: git clone https://github.com/ayulockin/llm-eval-sweep.git
2. Install dependencies: pip install -r requirements.txt
3. Configure your W&B credentials
4. Run example evaluations: python qa_full_sweeps.py

For more details, check out the GitHub repository.