Evaluating Large Language Models

Introduction

• Exploration of evaluating large language models (LLMs).
• Importance: New releases often claim surpassing benchmarks like GPT-4.
• Challenge: Understanding and utilizing evaluation benchmarks.

How to Evaluate LLMs

• Manual Evaluation

  • Acquire datasets (e.g., Truthful QA from Hugging Face).
  • Use Python programs, loops, and models to process data.
  • Tedious due to extensive coding requirements.

• Automated Tools and Libraries

  • Open source tools available to simplify evaluation.
  • Tools provide high-level APIs for easier integration.

Key Tools for Evaluation

• Evaluation Harness

  • GitHub repository by EleutherAI.
  • Framework for evaluating language models with few-shot evaluation.
  • Setup requires 15GB RAM/Vram using Google Colab Pro.

• Optimum Benchmark

  • Another popular tool for LLM evaluation (covered in next video).

Using Evaluation Harness

• Setup Instructions

  • Install from source using pip.
  • Optionally install 'bitsandbytes' for quantized model evaluation.

• Task List and Selection

  • Evaluation harness provides a list of tasks.
  • Tasks include Yahoo Answers, H-Swag, Truthful QA, etc.
  • Selection based on model's use-case and intended evaluation.

Understanding Specific Data Sets

• H-Swag (Commonsense NLI Dataset)

  • Developed by Allen Institute for AI.
  • Novel data collection via adversarial filtering.
  • Challenges models with machine-generated wrong answers.

• Truthful QA

  • Measures truthfulness in LLM-generated answers.
  • Spans categories like health, finance, politics.
  • Useful for evaluating factual accuracy of models.

Evaluation Process

• General Steps

  • Install evaluation harness and select tasks.
  • Load pre-trained models from Hugging Face (e.g., LLaMA 3).
  • Define evaluation parameters (e.g., batch size, device).
  • Execute model evaluations using chosen datasets.

• Analysis and Results

  • Evaluation results stored in JSON format.
  • Key metrics include accuracy, precision, recall, Rouge, BLEU scores.
  • Important to understand these metrics for effective evaluation.

Publishing Results

• Hugging Face Leaderboards
  • Models can be submitted to open LLM leaderboards.
  • Requirements: use of safe tensor format, open license, etc.
  • Leaderboards enable showcasing model performance.

Conclusion

• Tools like Evaluation Harness simplify LLM evaluation.
• Publishing results on leaderboards provides visibility.
• Encouraged to familiarize with evaluation metrics and benchmarks.