Case Study: Building and Refining an LLM Evaluation System

Accurately evaluating and comparing Large Language Models (LLMs) is crucial for selecting the right tool for a given task, yet it presents significant practical challenges. Standard benchmarks like MMLU exist, but simply running a model isn't enough.

This project initially aimed to gain hands-on experience by building a functional evaluation framework capable of running models like Llama 3.1 8B against the MMLU benchmark. However, upon achieving initial results (67.0 macro average), a new challenge emerged: the score was substantially lower than Meta's published 73.0 score.

This discrepancy highlighted several key difficulties:

• The need for a robust, flexible framework to conduct evaluations systematically.
• The sensitivity of LLM performance to subtle variations in prompting and evaluation methodology.
• The difficulty in exactly reproducing published benchmark scores due to potential differences in implementation details.
• Understanding the factors that influence benchmark results beyond the model itself.

The project proceeded in two main phases:

Phase 1: Evaluation Framework Development

• Design: Developed a Python-based evaluation framework using an orchestrator pattern to handle benchmark-specific logic (starting with MMLU).
• Technology Stack: Utilized Hugging Face Hub for model/tokenizer access, Transformers for inference, and PyTorch for calculations. Google Colab (A100/L4 GPU) was used for execution.
• Core Functionality: Implemented logic for loading benchmarks, formatting prompts (initially 0-shot), running model inference, extracting probabilities via logits (constrained decoding), and calculating accuracy scores.
• Initial Run: Successfully ran Llama 3.1 8B Instruct on MMLU, establishing a baseline score.

Phase 2: Discrepancy Investigation & Prompt Refinement

• Framework Refinement: Refactored the framework to easily configure and test different prompt structures, moving away from hardcoded prompts.
• Systematic Prompt Testing: Evaluated several prompt variations:
  • A basic instructional prompt.
  • A chat-template based prompt inspired by recent research.
  • A prompt designed to mimic the "original MMLU prompt" structure.
• Evaluation Method Consideration: Investigated potential differences between constrained decoding (logit-based) and open-ended generation, based on descriptions in Meta's documentation.
• Analysis: Compared the scores produced by different prompts to understand sensitivity and identify more effective structures.

This two-phased project yielded both a functional tool and critical insights:

Key outcomes and impacts:

• Framework Capability: Successfully developed a reusable framework for benchmarking LLMs.
• Prompt Sensitivity Demonstrated: Testing revealed significant variations in MMLU scores based solely on prompt structure (ranging from 61.0% to 68.3%), underscoring the critical nature of prompt engineering.
• Improved Baseline: Identified a more effective prompt ("original MMLU" structure) that improved the score from 67.0% to 68.3%.
• Reproducibility Insights: While the published 73.0% score wasn't exactly matched, the investigation highlighted the likely role of subtle, undocumented implementation details (specific prompt nuances, exact generation strategy) in achieving top benchmark scores.
• Enhanced Expertise: Gained deep practical understanding of the complexities involved in LLM evaluation, going beyond surface-level execution.

This project serves as a practical demonstration of the meticulous approach required for reliable LLM evaluation and the significant impact of careful prompt design.

This project reinforced several important principles for working with LLMs:

• Evaluation is Foundational: Building reliable systems requires the ability to measure performance systematically. Custom frameworks can provide necessary control and flexibility.
• Prompts Matter Immensely: LLM performance is highly sensitive to prompt structure and wording. Effective prompt engineering is crucial but can be complex.
• Reproducibility is Hard: Exactly matching published benchmark scores can be challenging due to subtle, often undocumented, differences in evaluation setup and prompting strategies.
• Look Under the Hood: Never blindly trust default templates or assumptions. Understanding the exact input being sent to the model is critical for debugging and optimization.
• Focus on Relative Performance: While striving for accuracy is important, comparing models or prompts *within the same consistent evaluation framework* often provides more actionable insights than chasing exact published scores.

The experience gained from building this evaluation framework and investigating benchmark discrepancies directly translates into value for client projects:

• Rigorous Model Selection: Ability to set up custom evaluations to compare different models (commercial or open-source) specifically on data relevant to the *client's* task, not just generic benchmarks.
• Informed Prompt Engineering: Deep understanding of how prompt structure impacts performance, leading to more effective and reliable prompts for client applications.
• Realistic Expectations: Ability to advise clients on the achievable performance and the inherent variability in LLM results, setting realistic project goals.
• Troubleshooting Expertise: Experience in diagnosing performance issues related to prompts or evaluation setup.
• Focus on Business Value: While benchmarks are useful, the ultimate goal is building solutions that solve real business problems. This experience reinforces the need to define and measure success based on client-specific metrics.