Skip to content

Prompt

What evaluation benchmarks and frameworks exist for objectively comparing the accuracy of two LLMs?

Output

To objectively compare the accuracy and performance of two large language models (LLMs), several evaluation benchmarks and frameworks are commonly used. These benchmarks involve standardized tasks, datasets, and metrics that allow for a fair comparison of LLMs across various dimensions such as accuracy, reasoning ability, and task-specific performance.

Key Evaluation Benchmarks

  1. MMLU (Massive Multitask Language Understanding):
  2. MMLU is one of the most comprehensive benchmarks designed to evaluate the multitask accuracy of LLMs across 57 tasks from diverse domains such as STEM, humanities, social sciences, and more. It assesses the model's ability to handle both general knowledge and specific problem-solving tasks in zero-shot and few-shot settings[1][2].

  3. Metrics: Coherence, relevance, detail, clarity.

  4. GLUE (General Language Understanding Evaluation):

  5. GLUE is a widely-used benchmark that tests an LLM’s ability to understand language through nine different tasks, including sentiment analysis, question answering, and sentence similarity. It provides a single score summarizing the model's performance across these tasks[5].

  6. Metrics: Accuracy, F1 score.

  7. SuperGLUE:

  8. An extension of GLUE, SuperGLUE includes more complex tasks that challenge models with advanced reasoning and comprehension problems. It was introduced after models began surpassing human-level performance on GLUE[5].

  9. Metrics: Accuracy, F1 score.

  10. TruthfulQA:

  11. This benchmark evaluates the truthfulness of LLMs by testing their ability to avoid generating human-like falsehoods. It includes both generation and multiple-choice tasks aimed at assessing how well a model can produce accurate and informative responses[1][5].

  12. Metrics: Truthfulness percentage, informativeness.

  13. HellaSwag:

  14. HellaSwag is a benchmark that tests commonsense reasoning by asking models to complete sentences or sequences in a way that is logically consistent with prior context. It focuses on evaluating a model’s understanding of everyday situations[5].
  1. DeepEval:
  2. DeepEval is an open-source framework that provides 14+ evaluation metrics for LLMs, including metrics for summarization, hallucination detection, faithfulness, contextual relevancy, and bias. It supports both Retrieval-Augmented Generation (RAG) pipelines and fine-tuning use cases[3][6].

  3. Metrics: Answer relevancy, contextual recall/precision, hallucination detection.

  4. RAGAs (Retrieval-Augmented Generation Assessment):

  5. RAGAs is specifically designed for evaluating RAG-based pipelines. It focuses on core metrics like faithfulness to retrieved information and contextual relevancy[3][6].

  6. Metrics: Faithfulness, contextual precision/recall.

  7. Eleuther AI:

  8. This framework supports over 200 evaluation tasks and 60+ benchmarks. Eleuther AI powers Hugging Face’s Open LLM Leaderboard and allows for custom prompts and evaluation metric integration[3].

  9. Metrics: Task-specific accuracy across a wide range of domains.

  10. MLFlow LLM Evaluate:

  11. MLFlow provides a modular framework for evaluating LLMs with support for RAG pipelines and question-answering tasks. It allows developers to integrate custom evaluation pipelines with ease[6].

  12. Metrics: QA correctness, answer relevancy.

  13. HumanEval (OpenAI):

  14. HumanEval is used to assess code generation capabilities by testing whether generated code passes predefined test cases[4]. It's particularly useful for evaluating models designed for programming tasks.
  15. Metrics: Code correctness (pass/fail rate).

Common Metrics Across Benchmarks

  • Perplexity: Measures how well the model predicts a sample of text based on probability distributions. Lower perplexity indicates better performance in language modeling but does not directly assess output quality or coherence[5].

  • Answer Relevancy: Evaluates whether the model's output directly addresses the input query in an informative manner[2].

  • Hallucination Detection: Checks if the output contains fabricated or incorrect information that wasn't part of the input context[2][6].

  • Bias/Toxicity Metrics: Used to ensure that models do not produce harmful or offensive content[2][6].

Conclusion

To objectively compare two LLMs' accuracy and overall performance, using a combination of benchmarks like MMLU, GLUE/SuperGLUE, TruthfulQA, and frameworks like DeepEval or Eleuther AI offers comprehensive insights into their strengths and weaknesses across various tasks. These tools provide standardized metrics such as accuracy, faithfulness, hallucination detection, and truthfulness—allowing for fair comparisons between different models.