LLM Performance: Building An Evaluation Framework

In the rapidly evolving landscape of Large Language Models (LLMs), establishing a robust evaluation framework is crucial. This framework is essential for objectively comparing the performance of various LLMs, such as GPT-4, GPT-3.5, and Claude, ensuring the selection of the most suitable model for specific tasks. This article delves into the intricacies of creating such a framework, outlining the key steps, metrics, and considerations involved. We will explore the methodology and results documentation, the development of automated evaluation runners, and the implementation of grading accuracy testers. Ultimately, this framework will empower us to make data-driven decisions, optimize prompts, and contribute to the advancement of LLM technology. Before we dive into the practical aspects, let's address the fundamental question: Why do we need an evaluation framework in the first place?

Why Evaluate LLM Performance?

In the realm of Large Language Models (LLMs), the importance of evaluation cannot be overstated. For both academic validation and practical application, a comprehensive evaluation framework serves several critical purposes. First and foremost, it allows us to compare different LLMs (such as GPT-4, GPT-3.5, and Claude) based on their ability to generate high-quality questions. This comparison extends beyond mere question generation, encompassing the consistency and accuracy of grading across these models. Furthermore, an evaluation framework enables the measurement of performance across various question types, including single-choice, multiple-choice, and open-ended questions. This granular analysis is essential for understanding the strengths and weaknesses of each model in different contexts. Beyond the immediate comparison of models, a well-defined evaluation framework provides a structured methodology for documenting results, which is crucial for academic pursuits like thesis writing and research paper publication. This documentation ensures transparency and reproducibility, fostering trust in the findings. The significance of this framework can be distilled into several key benefits.

Firstly, it provides the justification for model selection using empirical data. Rather than relying on subjective opinions or anecdotal evidence, the framework offers concrete metrics to support the choice of one model over another. Secondly, it helps in identifying the best models for specific question types. Some models may excel at generating multiple-choice questions, while others may be better suited for open-ended inquiries. The evaluation framework reveals these nuances, allowing for optimized deployment of LLMs. Thirdly, the framework facilitates the improvement of prompts based on evaluation results. By analyzing the performance of models under different prompts, we can refine our prompting techniques to elicit the best possible responses. Finally, the framework enables the creation of reproducible benchmarks, which are essential for tracking progress and ensuring the long-term reliability of LLMs. By establishing clear, repeatable evaluation procedures, we can build a solid foundation for the continued advancement of these powerful tools.

What Should an LLM Evaluation Framework Do?

An effective LLM evaluation framework should encompass a range of functionalities to provide a comprehensive assessment of model performance. The primary goal is to create a system that can run question generation with multiple LLM backends, enabling a comparative analysis of their capabilities. This involves not only generating questions but also evaluating their quality using predefined metrics. The framework should also be capable of testing grading accuracy and consistency, ensuring that the models can not only generate questions but also assess responses fairly and reliably. Ultimately, the framework should generate comparison reports and scores, providing a clear and concise overview of the relative strengths and weaknesses of each model. To achieve these objectives, the framework must deliver several key components. These deliverables serve as tangible outputs that demonstrate the framework's capabilities and provide valuable insights into LLM performance.

One of the most crucial deliverables is a detailed methodology and results document, typically formatted as docs/evaluations_of_responses.md. This document should outline the evaluation process, the metrics used, and the findings obtained from the analysis. It serves as a comprehensive record of the evaluation, ensuring transparency and reproducibility. Another essential component is an automated evaluation runner, implemented as a script such as scripts/evaluate_generation.py. This script automates the process of generating questions and evaluating their quality, reducing the manual effort required and ensuring consistency in the evaluation process. Similarly, a grading accuracy tester, such as scripts/evaluate_grading.py, is needed to assess the ability of the models to accurately grade responses. This tester should compare the model's grading with a gold standard, providing a measure of grading accuracy. In addition to these scripts and documents, the framework should include scoring rubrics for each question type. These rubrics provide clear criteria for evaluating the quality of questions and responses, ensuring that the evaluation is objective and consistent. Finally, the framework should generate comparison tables and visualizations, which offer a concise and intuitive way to present the evaluation results. These tables and visualizations should highlight the key differences in performance between the models, facilitating informed decision-making.

How to Build an LLM Evaluation Framework

Constructing an LLM evaluation framework involves a series of well-defined steps, each contributing to the overall robustness and effectiveness of the system. These steps encompass the design of evaluation metrics, the creation of evaluation scripts, the establishment of a scoring schema, the development of documentation, and the implementation of specific tasks. Let's delve into each of these aspects in detail to understand the intricacies of building such a framework. The first crucial step is to design evaluation metrics. These metrics serve as the cornerstone of the framework, providing quantifiable measures of LLM performance. The metrics should be tailored to the specific aspects of performance being evaluated, such as question generation and grading accuracy.

For question generation, several key metrics are essential. Answerability, rated on a scale of 0 to 1, assesses whether the generated question can be answered using the provided source material. A score of 1 indicates that all the necessary information is present in the source, while a score of 0 suggests that the question cannot be answered from the source. Clarity, also rated on a scale of 0 to 1, measures the unambiguity of the question. A clear question should have a single, well-defined answer, minimizing the potential for misinterpretation. Difficulty alignment, again on a scale of 0 to 1, evaluates whether the difficulty of the question matches the requested level. This is particularly important in educational settings, where questions should be appropriately challenging for the target audience. Distractor quality, rated from 0 to 1, is relevant for multiple-choice questions. It assesses whether the incorrect options are plausible but clearly wrong, ensuring that the question effectively tests the knowledge of the respondent. Source accuracy, another metric on a 0-1 scale, verifies that the source references provided with the question point to relevant content. This is crucial for ensuring that the question is grounded in the source material and can be verified. For open-ended questions, additional metrics are needed to capture the nuances of this question type. Reference answer quality, rated from 0 to 1, assesses whether the reference answer provided is complete and accurate. A high-quality reference answer serves as a benchmark for evaluating student responses. Rubric specificity, also on a 0-1 scale, measures whether the rubric items are measurable and well-defined. A specific rubric ensures that the grading process is objective and consistent. Coverage, rated from 0 to 1, evaluates whether the rubric items cover all the key points relevant to the question. A comprehensive rubric ensures that all important aspects of the response are considered. In addition to question generation, the framework must also evaluate grading performance.

For grading, several key metrics are used. Accuracy, measured on a scale of 0 to 1, compares the AI's grading to that of a human expert. A high accuracy score indicates that the AI's grading aligns with human judgment. Consistency, also rated from 0 to 1, assesses whether the same answer is graded the same across multiple runs. This is crucial for ensuring fairness and reliability in the grading process. Partial credit fairness, on a 0-1 scale, evaluates whether the AI gives appropriate credit for partially correct answers. This metric is particularly important for questions where there is a spectrum of possible responses. Feedback quality, rated from 0 to 1 and applicable only to open-ended questions, assesses whether the feedback provided by the AI is actionable and specific. High-quality feedback helps students understand their mistakes and improve their understanding. With the evaluation metrics defined, the next step is to create evaluation scripts. These scripts automate the process of generating questions, evaluating their quality, and assessing grading accuracy. Two key scripts are needed: scripts/evaluate_generation.py and scripts/evaluate_grading.py. The scripts/evaluate_generation.py script is designed to evaluate the question generation capabilities of LLMs. This script should accept a list of OpenAI-compatible models as input and generate a specified number of exams from the same source material using each model. The script should then apply the evaluation rubrics to the generated questions, assigning scores based on the defined metrics. The scores should be outputted in both JSON format and as markdown tables, facilitating easy analysis and reporting. Finally, the script should calculate the mean and standard deviation for each metric, providing a statistical summary of the results. The usage of this script can be exemplified as follows:

# Usage: python scripts/evaluate_generation.py --models gpt-4,gpt-3.5-turbo,claude-3-sonnet --runs 5

The scripts/evaluate_grading.py script, on the other hand, focuses on evaluating the grading accuracy of LLMs. This script should compare the AI's grading against human expert answers, providing a measure of grading accuracy. It should also test the consistency of the AI's grading by evaluating the same answer multiple times. The script should assess the AI's partial credit logic, ensuring that it assigns appropriate credit for partially correct answers. For open-ended questions, the script should also measure the quality of the feedback provided by the AI. The usage of this script can be illustrated as follows:

# Usage: python scripts/evaluate_grading.py --exam exam_123.json --reference ground_truth.json

After creating the evaluation scripts, the next step is to design a scoring schema. The scoring schema defines the output format for the evaluation results, ensuring that the data is structured and easy to analyze. A common output format is JSON, which is both human-readable and machine-parsable. The output should include metadata about the evaluation, such as the source file used, the timestamp of the evaluation, and the number of runs per model. The output should also include the results for each model, broken down by question generation and grading. For question generation, the results should be further broken down by question type (e.g., single-choice, multiple-choice, open-ended). For each metric, the output should include the mean and standard deviation, providing a statistical summary of the results. An example output format is as follows:

{
 "metadata": {
 "source_file": "examples/sample_medical.md",
 "timestamp": "2024-01-15T10:30:00Z",
 "runs_per_model": 5
 },
 "models": {
 "gpt-4o-mini": {
 "question_generation": {
 "single_choice": {
 "answerability": {"mean": 0.92, "std": 0.05},
 "clarity": {"mean": 0.88, "std": 0.07},
 "difficulty_alignment": {"mean": 0.85, "std": 0.09},
 "distractor_quality": {"mean": 0.78, "std": 0.12}
 },
 "multiple_choice": { ... },
 "open_ended": { ... }
 },
 "grading": {
 "accuracy": {"mean": 0.95, "std": 0.03},
 "consistency": {"mean": 0.98, "std": 0.02},
 "feedback_quality": {"mean": 0.82, "std": 0.11}
 }
 },
 "gpt-3.5-turbo": { ... },
 "claude-3-sonnet": { ... }
 }
}

With the scoring schema in place, the next step is to develop documentation. Documentation is crucial for ensuring that the evaluation framework is transparent and reproducible. The documentation should outline the evaluation methodology, the metrics used, and the results obtained. It should also provide instructions on how to run the evaluation scripts and interpret the results. The documentation should be written in a clear and concise manner, making it accessible to a wide audience. A key document is docs/evaluations_of_responses.md, which should provide a comprehensive overview of the evaluation process and its findings. This document should include sections on the evaluation setup, question generation metrics, grading accuracy, analysis and recommendations, and reproducibility. The structure of this document can be exemplified as follows:

# LLM Evaluation Methodology and Results

## 1. Evaluation Setup
- Source material: examples/sample_medical.md
- Models tested: GPT-4, GPT-3.5-turbo, Claude-3-sonnet
- Runs per model: 5
- Total questions generated: 100 per model (20q × 5 runs)

## 2. Question Generation Metrics

### Single Choice Questions
| Model | Answerability | Clarity | Difficulty | Distractor Quality | Overall |
|-------|--------------|---------|------------|-------------------|---------|
| GPT-4 | 0.92 ± 0.05 | 0.88 ± 0.07 | 0.85 ± 0.09 | 0.78 ± 0.12 | 0.86 |
| GPT-3.5 | ... | ... | ... | ... | ... |

### Open-Ended Questions
| Model | Answerability | Ref Answer Quality | Rubric Specificity | Overall |
|-------|--------------|-------------------|-------------------|---------|
| GPT-4 | ... | ... | ... | ... |

## 3. Grading Accuracy

### Methodology
- Ground truth: Expert-graded answers (medical educator)
- Test set: 30 student responses per question type
- Metrics: Accuracy, consistency, feedback quality

### Results
| Model | Accuracy | Consistency | Feedback Quality | 
|-------|----------|------------|------------------|
| GPT-4 | 0.95 ± 0.03 | 0.98 ± 0.02 | 0.82 ± 0.11 |

## 4. Analysis & Recommendations

### Best Practices
- GPT-4 recommended for open-ended generation (higher rubric quality)
- GPT-3.5 sufficient for single-choice (cost-effective)
- All models achieve >90% grading accuracy

### Limitations
- Sample size: 100 questions per model
- Single domain: medical education (obstetrics)
- Language: Russian only

## 5. Reproducibility
```bash
# Reproduce evaluation
python scripts/evaluate_generation.py --models gpt-4,gpt-3.5-turbo --runs 5
python scripts/evaluate_grading.py --exam evaluations/test_exam.json

The final step in building the evaluation framework is to implement specific tasks. These tasks involve setting up the necessary infrastructure, implementing the evaluation logic, and generating reports. The implementation tasks can be divided into several phases. **Phase 1** focuses on setting up the infrastructure. This involves creating the `scripts/` directory for the evaluation scripts, creating the `evaluations/` directory for the results, adding evaluation dependencies to the requirements.txt file, and creating base evaluation classes. **Phase 2** focuses on question generation evaluation. This involves implementing the answerability scorer, the clarity scorer, the difficulty alignment checker, and the distractor quality scorer. It also involves creating the `evaluate_generation.py` script. **Phase 3** focuses on grading evaluation. This involves creating a ground truth dataset of expert answers, implementing the consistency tester, implementing the accuracy comparison, implementing the feedback quality scorer, and creating the `evaluate_grading.py` script. **Phase 4** focuses on reporting. This involves creating a JSON output formatter, a markdown table generator, performing statistical analysis (mean, std, confidence intervals), and optionally creating visualizations (e.g., using matplotlib charts). **Phase 5** focuses on documentation. This involves writing the methodology section, running evaluations on all models, documenting the results and analysis, and adding recommendations and limitations.

### Technical Considerations

Several **technical considerations** are paramount when building an LLM evaluation framework. These considerations ensure that the framework is robust, scalable, and cost-effective. One of the key considerations is multi-backend support. The framework should be able to support multiple LLM backends, such as OpenAI, Anthropic, and local models. This requires abstracting the LLM client to handle different APIs and authentication methods. Environment variables should be used to store API keys (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY), allowing for easy configuration and security. The framework should also handle rate limits and retries, ensuring that evaluations can run smoothly even under heavy load. Another important consideration is the use of an LLM-as-judge approach. This involves using a powerful LLM, such as GPT-4, as the