LLM Benchmarks

What are LLM Benchmarks?

In the rapidly evolving landscape of natural language processing (NLP), new LLMs are emerging at an astonishing pace. GPT-4o by OpenAI, Claude-3 Opus by Anthropic, Le Large by Mistral, and Gemini Ultra 1.5 by Google are a few recent LLM releases. These models promise to revolutionize tasks such as text generation, sentiment analysis, question answering, and more. However, with so many LLMs popping up like mushrooms, how do we objectively compare their performance?

LLM benchmarks serve as standardized evaluation frameworks for assessing language performance. They provide a consistent way to measure various aspects of an LLM’s capabilities, including accuracy, efficiency, and generalization across tasks such as reasoning, truthfulness, code generation, different languages, etc. They provide a fair comparison, help gauge generalization across domains, guide model selection, track progress, and foster community collaboration.

Different Types of LLM Benchmarks

Below are some of the most prominent LLM benchmarks used to assess various aspects of a model’s capabilities.

Reasoning and Commonsense:

These benchmarks test an LLM’s ability to understand context and reasoning and apply logic and everyday knowledge to solve problems. Popular benchmarks include:

  • HellaSwag tests commonsense inference by completing video captions with plausible endings, using “Adversarial Filtering” to challenge models with deceptive options.
  • DROP evaluates models on reading comprehension and discrete reasoning, requiring actions like sorting and counting from text.

Truthfulness and Question Answering (QA):

These evaluate a model’s ability to interpret text accurately and generate truthful, reliable answers by testing the model’s capability to distinguish factual statements from falsehoods and handle complex, domain-specific questions. Common benchmarks are:

  • TruthfulQA measures whether models generate truthful answers, focusing on avoiding human-like falsehoods.
  • GPQA is an expert-level, Google-proof QA benchmark designed with extremely difficult questions across domains such as biology and physics.
  • MMLU assesses knowledge and reasoning across various tasks, including mathematics, history, and law, using zero-shot and few-shot settings.

Math Benchmarks:

These focus on testing mathematical reasoning and problem-solving capabilities ranging from basic arithmetic to advanced topics like algebra, calculus, and statistics. They assess how well models perform calculations, understand mathematical concepts, and apply logic to solve complex problems. Some existing benchmarks are:

  • GSM-8K tests a model’s ability to solve grade-school-level math word problems, focusing on understanding and applying basic arithmetic and logical reasoning.
  • MATH evaluates a model’s proficiency in mathematical reasoning across a range of topics, from basic arithmetic to advanced subjects like algebra and calculus.

Coding Benchmarks:

Coding benchmarks assess a language model’s ability to understand and generate code accurately. These benchmarks typically cover various programming languages and tasks, ranging from simple algorithmic problems to complex software development challenges. HumanEval is a benchmark used to evaluate LLMs trained on code focusing on measuring the functional correctness of synthesized programs generated from docstrings.

Conversation and Chatbots:

These test an LLM’s ability to engage in natural, human-like conversations and provide relevant responses. These benchmarks often include tasks such as dialogue generation, response ranking, and conversational understanding. Chatbot Arena is a popular open platform for evaluating LLMs by human preference and feedback.


LLM Benchmarks

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison

Challenges in LLM Benchmarks

  • Prompt sensitivity: Specific prompts can influence metrics, masking true model capabilities.
  • Construct validity: Defining acceptable answers across diverse use cases is complex due to the wide range of tasks.
  • Limited scope: Existing benchmarks may not effectively assess LLMs on future skills or specific capabilities.
  • Standardization gap: Lack of benchmark standardization leads to inconsistencies in evaluation results.
  • Human evaluations: Subjective human evaluations are time-consuming and costly, impacting tasks like abstractive summaries.

LLM Benchmark Evaluators

There are several leaderboards that compare LLM models across diverse tasks, from chatbot interactions to complex games, using different benchmarks.

  • Open LLM Leaderboard by Hugging Face: This comprehensive collection tracks, ranks, and evaluates open LLMs and chatbots. It covers a wide range of tasks, including text generation, question answering, and sentiment analysis.
  • Big Code Models Leaderboard by Hugging Face: Focused on multilingual code generation models, this benchmark evaluates performance on the HumanEval benchmark and MultiPL-E and considers throughput.
  • Simple-evals by OpenAI: A lightweight library developed by OpenAI for evaluating their models like gpt-4-turbo-2024-04-09 and gpt-4o against other SOTA models like BERT and Claude. It emphasizes zero-shot and chain-of-thought evaluation, covering benchmarks such as MMLU, MATH, GPQA, DROP, MGSM, and HumanEval.
LLM Benchmark Evaluators

(Text-based LLM evaluation – Source)