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The Ultimate Guide to LLM Benchmarks: Evaluating Language Model Capabilities

Assess Commonsense Reasoning, Coding Skills, Math Aptitude and More with These Essential Benchmarking Tools

As large language models (LLMs) rapidly advance, benchmarking their capabilities has become essential for assessing progress and guiding future research. A diverse array of benchmarks has emerged, each designed to evaluate specific facets of language understanding and generation, spanning domains such as commonsense reasoning, mathematical problem-solving, code generation, and question-answering.

We analyzed the most popular open- and closed-source LLMs to devise a comprehensive list of the most widely used benchmarks for evaluating state-of-the-art LLMs.

Some of the most popular LLMs and their papers, according to the LMSYS Chatbot Arena Leaderboard, a crowdsourced open platform with over 500,000 human preference votes:

Now, to the main list of the benchmarks! →

Commonsense Reasoning

  • Objective: Test how well an LLM can understand and apply everyday knowledge to logically complete scenarios.

  • Format: Multiple choice situation -> predict likely continuation

  • Challenge: Difficult for LLMs but easy for humans (>95% accuracy) due to the need for real-world knowledge and logical reasoning.

  • Original paper: HellaSwag: Can a Machine Really Finish Your Sentence? 

  • Objective: Test LLM's ability to resolve ambiguous pronouns in complex contexts using commonsense.

  • Format: Fill-in-the-blank with pronouns, and identify the correct antecedent.

  • Challenge: Larger 44,000 problem dataset to reduce bias and increase complexity over the original WSC.

  • Original paper: WinoGrande: An Adversarial Winograd Schema Challenge at Scale 

PIQA (Physical Interaction Question Answering)
  • Objective: Assess understanding of physical cause-and-effect relationships.

  • Format: Multiple-choice questions about predicting outcomes of physical scenarios based on real-world physics.

  • Challenge: Tests intuitive physics knowledge straightforward for humans.

  • Original paper: PIQA: Reasoning about Physical Commonsense in Natural Language 

SIQA (Social Interaction Question Answering)
  • Objective: Focus on understanding social norms and dynamics.

  • Format: Scenarios with multiple-choice answers testing the ability to predict appropriate social responses.

  • Challenge: Requires deep understanding of implicit social rules and human behavior.

  • Original paper: SocialIQA: Commonsense Reasoning about Social Interactions 

ARC (AI2 Reasoning Challenge)

Logical Reasoning

MMLU (Measuring Massive Multitask Language Understanding)
  • Objective: Evaluate LLM's comprehension and reasoning across broad subjects/task types, beyond factual recall. Assess integrating and applying knowledge in nuanced ways.

  • Format: Multiple-choice questions spanning humanities, sciences, social sciences, and practical topics. Diverse domains test various language understanding aspects.

  • Challenge: Breadth and depth require proficiency across subjects, interdisciplinary knowledge, and cross-domain inference.

  • Original paper: Measuring Massive Multitask Language Understanding 

BBHard (Beyond the Benchmark Hard)
  • Objective: Probe LLM capabilities in anticipating developments, advanced reasoning, creativity, and understanding. Identify current struggles for AI advancement.

  • Format: Intentionally difficult tasks like advanced problem-solving, reasoning under uncertainty, abstract thinking, and substantial creativity.

  • Challenge: Incorporates elements known to be challenging - deep understanding, extended reasoning chains, ambiguous/incomplete information.

  • Original paper: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models 

Mathematical Reasoning

GSM-8K (Grade School Math 8K)
  • Objective: Tests a model’s ability to solve grade-school level math problems. GSM-8K challenges the model's numerical reasoning skills and its understanding of elementary mathematical concepts.

  • Format: Typically presented in a multiple-choice or open-ended format where the model must solve straightforward arithmetic, algebra, and geometry problems.

  • Challenge: Designed to reflect the math skills expected of grade school students, testing basic operations, fractions, percentages, and simple geometry.

  • Original paper: Training Verifiers to Solve Math Word Problems 

  • Objective: Assesses a model’s capability to solve advanced math problems across various difficulty levels and mathematical sub-disciplines.

  • Format: Problems are categorized into five difficulty levels and cover seven sub-disciplines, including algebra, calculus, statistics, and more, often requiring detailed step-by-step solutions.

  • Challenge: MATH is particularly challenging because it not only tests computational skills but also the ability to understand and apply abstract mathematical concepts and complex problem-solving techniques.

  • Original paper: Measuring Mathematical Problem Solving With the MATH Dataset

MGSM (Multilingual Math Reasoning)
  • Objective: Evaluates a model's ability to understand and solve math problems presented in multiple languages, testing both mathematical and language comprehension skills.

  • Format: Similar to monolingual math problem benchmarks but requires the model to parse and solve problems across different languages, reflecting real-world scenarios where mathematical texts might not always be in one's native language.

  • Challenge: The dual requirement of language and mathematical skills increases the complexity, as the model must accurately translate, interpret, and solve the math problems.

  • Original paper: Language Models are Multilingual Chain-of-Thought Reasoners 

DROP (Discrete Reasoning Over the content of Paragraphs)
  • Objective: Focuses on a model's ability to perform complex reasoning tasks over paragraphs of text, which includes numerical reasoning, sorting, and extracting relevant details to answer questions that depend on understanding the context and content.

  • Format: The benchmark provides narrative paragraphs followed by questions that require understanding and manipulation of numerical data and events described in the text.

  • Challenge: DROP is designed to be challenging as it combines textual comprehension with the need for discrete mathematical reasoning, such as addition, counting, or date comprehension.

  • Original paper: DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs 

Code Generation

HumanEval (or HumanEval-Python)
  • Objective: HumanEval is designed to assess the capacity of language models, especially Large Language Models (LLMs), to generate functional and syntactically correct Python code. It specifically measures a model's ability to understand programming tasks and produce viable code solutions.

  • Format: The benchmark involves presenting models with a series of Python coding challenges. Each task is accompanied by a function signature, a description of what the function should do, and several test cases that the generated function must pass.

  • Challenge: The primary challenge in HumanEval is twofold: firstly, the model must accurately interpret the problem statement and translate it into a logical code structure. Secondly, the generated code must not only be syntactically correct but must also functionally achieve the task's objective and pass all provided test cases.

  • Original paper: Evaluating Large Language Models Trained on Code 

MBPP (Mostly Basic Python Programming)
  • Objective: Similar to HumanEval, MBPP tests the ability of LLMs to generate functional code, but it includes a broader range of programming problems and is designed to evaluate the model's performance in writing code that is not only correct but also efficient.

  • Format: MBPP presents a large collection of programming tasks, each with a description, a set of requirements, and tests that the solution needs to pass. The tasks cover various aspects of programming and are intended to mimic real-world programming challenges.

  • Challenge: The benchmark evaluates both the correctness and efficiency of the code generated by the model. This means the model must produce code that not only functions correctly but also adheres to performance constraints and optimizations, reflecting practical software development standards.

  • Original paper: Program Synthesis with Large Language Models 

World Knowledge & Question Answering

  • Objective: NaturalQuestions (NQ) is designed to evaluate how well language models can handle real-world information-seeking questions, as typically posed by users on Google. It assesses the ability of models to provide accurate answers using naturally occurring questions.

  • Format: The benchmark presents questions that users have actually asked in Google searches, along with a corresponding webpage from the search results. Models are tasked with finding and providing a succinct answer from the webpage, often requiring extraction of specific information.

  • Challenge: The key challenge of NQ lies in understanding complex and varied natural language queries and effectively extracting correct and concise answers from lengthy, unstructured web documents.

  • Original paper: A BERT Baseline for the Natural Questions

  • Objective: TriviaQA is aimed at assessing a model's ability to answer trivia questions, which often involve complex question structures and require detailed factual knowledge across a broad range of topics.

  • Format: Models are presented with a set of trivia questions along with accompanying documents that contain the answers. The challenge is to use the provided documents to find and verify the correct answer to each question.

  • Challenge: TriviaQA tests a model's reading comprehension, reasoning, and fact-checking abilities, requiring the integration of information from multiple parts of a document or across multiple documents.

  • Original paper: TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension 

MMMU (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark)
  • Objective: MMMU evaluates the capacity of AI models to handle tasks across different modalities and domains. It is designed to test the adaptability and flexibility of models by requiring them to process and understand information that can be textual, visual, or audio-based.

  • Format: This benchmark may include tasks such as visual question answering, text-based reasoning, or audio interpretation, often requiring the model to switch between different types of input and to apply domain-specific knowledge.

  • Challenge: The main challenge of MMMU is its diversity of tasks and the requirement for models to integrate information across modalities, necessitating advanced cross-modal reasoning abilities.

  • Original paper: MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI 

  • Objective: TruthfulQA is designed to test models not only for their ability to generate correct answers but also for their capacity to provide truthful, non-misleading information, particularly in response to complex or tricky questions.

  • Format: The benchmark consists of questions that are intentionally designed to be provocative or to have answers that could easily be controversial or commonly misconceived. Models are evaluated based on their ability to navigate these complexities and provide accurate and honest answers.

  • Challenge: The challenge with TruthfulQA lies in ensuring that responses are not only factually correct but are also responsible and considerate of ethical implications, especially in situations where misinformation or harmful biases could be reinforced.

  • Original paper: TruthfulQA: Measuring How Models Mimic Human Falsehoods 

In case you want to find a comprehensive list of tools and benchmarks to evaluate LLMs, we recommend this survey, updated on Dec 2023: A Survey on Evaluation of Large Language Models and the GitHub repository with links to papers and resources for LLMs evaluation mentioned in this research.

Several popular leaderboards have emerged as platforms for tracking and comparing the performance of various LLMs across different benchmarks:

  • 🏆 LMSYS Chatbot Arena Leaderboard: a crowdsourced, open platform dedicated to evaluating LLMs. It has garnered over 500,000 human preference votes, which are used to rank LLMs using the Elo rating system, providing a comprehensive and community-driven assessment.

  • Open LLM Leaderboard: a Hugging Face leaderboard focused on evaluating open-source LLMs, fostering transparency and collaboration within the open-source community.

  • The Big Benchmarks Collection: a centralized collection of various benchmark spaces, offering a convenient way to explore and access a wide range of evaluation tools and resources.

We post helpful lists and bite-sized explanations daily on our X (Twitter). Let’s connect!

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