There are many benchmarks that have been created to evaluate the capabilities of language models (LLMs). These benchmarks test a wide variety of dimensions, such as reasoning, language understanding, common-sense knowledge, factual recall, and more. Here’s a comprehensive list of the most widely recognized LLM benchmarks, each with a focus on three key points:
1. GLUE (General Language Understanding Evaluation)
Technical Detail: A collection of nine natural language understanding (NLU) tasks, such as sentiment analysis, sentence similarity, and textual entailment.
Capabilities: Measures how well models understand natural language semantics across multiple tasks, providing a broad assessment of NLU.
Use Case: Primarily used for evaluating how well models perform on common NLU tasks. It serves as a standard benchmark for comparing the general understanding capability of language models.
Location: GLUE Benchmark Dataset
How to Use: Download the dataset from the official website. Fine-tune your model on the provided training data for each of the 9 tasks (e.g., MNLI, SST-2, QNLI). After training, submit your model predictions to the GLUE server for evaluation on the test set. You will receive a score that reflects the model's performance across all tasks.
2. SuperGLUE
Technical Detail: An updated and more challenging version of GLUE, with tasks like reading comprehension, word sense disambiguation, and coreference resolution.
Capabilities: Designed to be tougher for models that perform well on GLUE, it pushes limits on natural language understanding, especially on tasks requiring deeper reasoning.
Use Case: Used to evaluate models that are beyond the GLUE benchmark, giving researchers insight into more complex and advanced model capabilities.
Location: SuperGLUE Benchmark
How to Use:Access the dataset via the official website.Train your model on the more challenging tasks like Winograd, COPA, and MultiRC.Submit your predictions to the SuperGLUE server to receive your model's performance results.Use the results to evaluate advanced NLU capabilities and compare your model with state-of-the-art models.
3. SQuAD (Stanford Question Answering Dataset)
Technical Detail: Consists of questions and corresponding answers derived from Wikipedia articles, specifically designed for reading comprehension.
Capabilities: Measures a model's ability to extract precise answers from a given text. SQuAD 2.0 introduces unanswerable questions to further test model understanding.
Use Case: Useful for benchmarking question-answering systems and models designed to perform extractive tasks based on passages of text.
Location: SQuAD Dataset
How to Use:Download the SQuAD v1.1 or SQuAD v2.0 datasets.Fine-tune your model on the dataset (training involves providing answers to questions based on a given context).Evaluate your model using the provided validation and test sets.For SQuAD v2.0, handle unanswerable questions as well.Submit your predictions to the leaderboard for comparison with other models.
4. MS MARCO
Technical Detail: A large dataset with real-world question-answering tasks based on Bing search queries and answers.
Capabilities: Focuses on both reading comprehension and information retrieval. The dataset includes answers that are extracted from web documents, making it highly relevant for real-world search and QA systems.
Use Case: Typically used for models that focus on open-domain QA, information retrieval, and web search task evaluation.
Location: MS MARCO
How to Use:Access the datasets for tasks like passage retrieval, question-answering, and document ranking.Fine-tune your model for passage retrieval or question-answering tasks using the provided data.After training, submit your results to the MS MARCO leaderboard.Compare your model’s performance with other top-performing models on real-world search engine queries.
5. MMLU (Massive Multitask Language Understanding)
Technical Detail: Comprises 57 task categories, ranging from elementary-level knowledge to professional-level subjects like law and medicine.
Capabilities: Measures a model’s ability to generalize across a wide range of topics and domains, covering both academic and general knowledge areas.
Use Case: Evaluates how well language models generalize and reason across different domains, from basic math to complex professional knowledge.
Location: MMLU on GitHub
How to Use: Download the benchmark from the repository. Fine-tune your model across 57 different task categories that range from elementary knowledge to professional-level subjects. Run your model on the test sets provided for evaluation. Compare your results with other models, especially in specialized domains like law and medicine.
6. BIG-bench (Beyond the Imitation Game)
Technical Detail: A diverse set of over 200 tasks designed to test the reasoning, comprehension, and knowledge capacity of models, with a focus on tasks that are difficult for current models.
Capabilities: Tests a wide variety of tasks, including reasoning, common sense, creativity, and math, providing a broad measure of LLM's capabilities.
Use Case: Benchmarking large-scale models for tasks that go beyond traditional benchmarks, designed for models exceeding human performance in narrow tasks.
Location: BIG-bench GitHub
How to Use:Access over 200 diverse tasks from the GitHub repository.Fine-tune your model on a range of tasks including reasoning, commonsense understanding, creativity, and more.Submit your results to the BIG-bench leaderboard for evaluation.Use this benchmark for large models, especially those seeking to exceed human-level performance on narrow tasks.
7. OpenAI's TruthfulQA
Technical Detail: A dataset and benchmark specifically designed to test whether models can provide accurate and non-misleading answers, even on ambiguous or adversarial questions.
Capabilities: Measures factual accuracy and reasoning under adversarial questioning, assessing the model’s ability to avoid common misconceptions or falsehoods.
Use Case: Used to evaluate how truthful models are in providing information, especially in areas where misinformation or bias is likely.
Location: TruthfulQA GitHub
How to Use:Download the dataset designed to assess the factual accuracy of models.Fine-tune your model to avoid providing misleading or inaccurate answers, especially on ambiguous or tricky questions.Use the dataset to test your model’s responses in various contexts that often cause models to generate false information.Analyze the output to assess how truthful and reliable the model is.
8. HellaSwag
Technical Detail: A benchmark for commonsense reasoning that provides incomplete sentences followed by four possible continuations, of which only one is correct.
Capabilities: Tests the ability to handle situations requiring deep commonsense reasoning, especially in tasks related to understanding everyday scenarios.
Use Case: Used to evaluate how well models can complete sentences or understand narratives by leveraging commonsense knowledge.
Location: HellaSwag Dataset
How to Use:Download the dataset, which contains incomplete sentences with multiple-choice endings.Fine-tune your model to choose the correct sentence completion using commonsense reasoning.Submit your results to the HellaSwag leaderboard for comparison against other models.Evaluate how well your model understands everyday scenarios.
9. PIQA (Physical Interaction Question Answering)
Technical Detail: A dataset focusing on questions about physical commonsense reasoning, like the practicality of everyday objects and their usage.
Capabilities: Evaluates a model’s ability to reason about physical interactions, testing knowledge that would be crucial for robotics, automation, or grounded AI systems.
Use Case: Designed for applications where understanding physical environments and interactions is important, like robotics and assistive technologies.
Location: PIQA Dataset
How to Use:Download the dataset focused on physical reasoning.Train your model to answer multiple-choice questions about the practicality and function of everyday objects. Evaluate the model’s understanding of physical interactions. Submit results to the leaderboard for performance comparison with other models.
10. Winograd Schema Challenge (WSC)
Technical Detail: A commonsense reasoning test where models must resolve ambiguous pronouns in sentences based on world knowledge.
Capabilities: Tests coreference resolution and commonsense reasoning in a challenging setting where small contextual details matter.
Use Case: Benchmark for AI systems to measure their ability to apply world knowledge in linguistic tasks requiring precise reasoning.
Location: WSC Dataset
How to Use:Download the dataset consisting of ambiguous pronoun references.Fine-tune your model for coreference resolution by training it to resolve which entity a pronoun refers to.Evaluate your model using the validation set.Compare your model’s ability to resolve complex sentences with human-level understanding.
11. CodexEval
Technical Detail: A benchmark designed specifically for evaluating the code generation capabilities of language models using tasks from various programming languages.
Capabilities: Measures a model’s ability to write correct and functional code, debugging, and reasoning over programming tasks.
Use Case: Used for assessing code-generation models like Codex (part of GitHub Copilot), especially those focused on software development.
Location: OpenAI Codex API
How to Use:CodexEval is evaluated using OpenAI’s Codex API.Fine-tune your model on specific programming tasks across various languages.Use CodexEval for automatic code generation, debugging, and problem-solving challenges.Compare the performance of code-generation models on popular coding problems or real-world programming tasks.
12. ARC (AI2 Reasoning Challenge)
Technical Detail: A question-answering benchmark that tests scientific reasoning using elementary and high-school science exams.
Capabilities: Focuses on reasoning and knowledge application, especially in scientific domains.
Use Case: Useful for testing how well models can reason and answer factual scientific questions across multiple domains, from biology to physics.
Location: ARC Dataset
How to Use:Download the dataset containing elementary and high-school science questions.Fine-tune your model on the training data and evaluate its ability to reason and answer scientific questions.Submit results to the leaderboard to compare your model with other QA systems, especially on reasoning-based science questions.
13. CoQA (Conversational Question Answering)
Technical Detail: A benchmark for conversational question answering, where the questions are contextual and based on previous interactions.
Capabilities: Measures models' ability to understand context and maintain coherence across multiple turns in a conversation.
Use Case: Useful for evaluating models intended for chatbots or any application where maintaining dialogue coherence is important.
Location: CoQA Dataset
How to Use:Access the conversational dataset from the official website.Fine-tune your model to answer context-dependent questions based on previous dialogue turns.Use CoQA to test the model's ability to maintain dialogue coherence.Submit your model’s results to the CoQA leaderboard for evaluation.
14. LAMBADA
Technical Detail: A cloze-style task where the model must predict the final word of a paragraph, requiring broad context understanding.
Capabilities: Evaluates a model's ability to understand long-range dependencies and context, rather than just local sentence completion.
Use Case: Used to test how well a model can maintain coherence and context over longer stretches of text, useful in narrative generation or long-form dialogue systems.
Location: LAMBADA Dataset
How to Use:Download the dataset from GitHub.Train your model to predict the last word of a paragraph, requiring an understanding of the full context.Use LAMBADA to evaluate long-range dependency understanding in text.Analyze the model’s accuracy in predicting words based on the entire narrative context.
15. Race (Reading Comprehension Dataset from Exams)
Technical Detail: A dataset based on English exams taken by Chinese students, testing reading comprehension across middle and high-school levels.
Capabilities: Assesses a model's ability to comprehend and analyze complex reading material, especially academic texts.
Use Case: Targeted at evaluating reading comprehension and understanding in educational or knowledge-based systems.
Location: RACE Dataset
How to Use:Download the dataset containing middle and high-school level reading comprehension questions.Fine-tune your model to answer questions after reading academic passages.Evaluate the model on test data, comparing how well it understands complex text.Submit results to the leaderboard to assess comprehension performance.
16. MLQA (Multilingual Question Answering)
Technical Detail: A benchmark that tests multilingual models on question-answering tasks across multiple languages.
Capabilities: Measures how well models can perform on the same QA tasks across different languages, assessing both translation and understanding capabilities.
Use Case: Designed for evaluating multilingual models, useful in globalized settings where multiple languages need to be supported.
Location: MLQA Dataset
How to Use:Access the multilingual dataset from GitHub.Fine-tune your model for question-answering tasks in multiple languages.Use MLQA to evaluate your model’s ability to answer the same question in different languages.Compare performance across languages to test multilingual and translation capabilities.
17. TyDi QA
Technical Detail: A multilingual QA dataset covering 11 typologically diverse languages, emphasizing linguistic diversity.
Capabilities: Evaluates language models on diverse question-answering tasks where resources in many languages are scarce.
Use Case: Ideal for benchmarking models that aim to handle global, low-resource languages in question-answering tasks.
Location: TyDi QA Dataset
How to Use:Download the dataset covering 11 diverse languages.Fine-tune your model for question-answering tasks in low-resource languages.Evaluate the model’s performance on typologically diverse languages and question structures.Use this benchmark for testing multilingual language models in lesser-known languages.
18. CommonsenseQA
Technical Detail: A multiple-choice QA dataset designed to evaluate commonsense knowledge by asking questions based on real-world scenarios.
Capabilities: Measures a model's ability to apply commonsense knowledge, reasoning, and understanding of everyday situations.
Use Case: Primarily used for applications that require models to leverage real-world understanding, such as virtual assistants or automated reasoning systems.
Location: CommonsenseQA Dataset
How to Use:Access the dataset that contains multiple-choice questions based on real-world knowledge.Fine-tune your model to answer questions requiring commonsense reasoning.Submit your results to the CommonsenseQA leaderboard.Use the benchmark to evaluate models that need to demonstrate real-world, human-like reasoning.
19. DROP (Discrete Reasoning Over Paragraphs)
Technical Detail: A reading comprehension benchmark that requires models to perform discrete reasoning (like addition or sorting) over given passages.
Capabilities: Tests models' ability to reason with numbers, dates, and events in a textual context, going beyond simple text matching.
Use Case: Ideal for tasks that require a blend of textual understanding and numerical reasoning, such as financial or historical analysis.
Location: DROP Dataset
How to Use:Download the dataset from the Allen AI website.Fine-tune your model to answer questions that require discrete reasoning (e.g., adding, sorting) over text passages.Evaluate your model’s ability to combine text comprehension with mathematical reasoning.Submit results to the leaderboard for comparison with other reasoning models.
20. GSM8K
Technical Detail: A dataset designed to evaluate mathematical problem-solving capabilities, specifically targeting problems requiring multi-step reasoning.
Capabilities: Focuses on multi-step reasoning, logical deduction, and problem-solving in a structured format.
Use Case: Useful for testing models designed to handle mathematics, reasoning, and structured problem-solving tasks, which are applicable in both education and advanced AI reasoning tasks.
Location: GSM8K Dataset on GitHub
How to Use:Download the dataset focused on math problem-solving.Fine-tune your model for multi-step reasoning tasks, particularly in math problems.Use the dataset to evaluate structured reasoning, logic, and problem-solving abilities.Analyze your model’s accuracy on grade-school level math problems.
Key Observations:
Broad Coverage: The initial list includes the most widely cited benchmarks in the LLM landscape. Popular benchmarks like GLUE, SuperGLUE, SQuAD, and MS MARCO are foundational in measuring NLU (Natural Language Understanding) and question-answering capabilities. These benchmarks cover a broad range of model capacities and domains.
Task Specificity: The inclusion of specialized benchmarks like HellaSwag for commonsense reasoning, PIQA for physical interaction reasoning, and TruthfulQA for factual accuracy highlights the diversity of tasks that LLMs are tested against. These benchmarks test different aspects of model generalization beyond typical text understanding and are important for niche applications.
Missing/Newer Benchmarks:
MATH (Mathematics Understanding from Text): This benchmark is focused on evaluating models on complex mathematical reasoning tasks. It’s highly relevant for models like GPT-4 and others that are increasingly used in domains requiring advanced numerical reasoning.
MMLU is very important, but it could use a bit more explanation regarding its utility for testing generalization to professional domains like law, medicine, and more, as it has gained relevance recently with LLMs being applied in professional and high-stakes environments.
AGI-specific benchmarks like BIG-bench represent a new class of evaluation, particularly important for models aiming at general artificial intelligence. This benchmark could be emphasized for its role in pushing models beyond human performance.
Balanced Representation of Use Cases: While most benchmarks’ use cases were well explained, some could benefit from clearer definitions. For example, CoQA could explicitly mention that it tests for conversational consistency over multiple turns, which is vital for real-world applications like chatbots.
Key Refined:
SQuAD & MS MARCO: Both are standard benchmarks for QA, but emphasizing MS MARCO's focus on open-domain question-answering could help differentiate it from SQuAD, which is primarily for reading comprehension. This distinction is important as MS MARCO is more reflective of real-world search engine tasks, while SQuAD evaluates answer extraction from a specific passage.
GLUE & SuperGLUE: These benchmarks are foundational for language understanding, but it would be helpful to elaborate that SuperGLUE introduces more complex tasks, making it more suited to models that have moved beyond GLUE’s limitations. This would give more context on why a model should progress from GLUE to SuperGLUE.
Winograd Schema Challenge (WSC): This is important for testing commonsense reasoning and coreference resolution. Clarifying that WSC is particularly difficult for models due to its reliance on subtle contextual details, which models often struggle with, would make its utility clearer.
TruthfulQA: This benchmark specifically tests the model's ability to avoid false or misleading information, especially in adversarial contexts. As truthfulness and reliability of LLMs have become more critical, TruthfulQA is gaining importance, especially for fact-checking and applications where misinformation could have real-world consequences.
Mathematics and Reasoning: A benchmark like GSM8K is crucial for evaluating models on multi-step mathematical reasoning. However, including a benchmark like MATH for assessing complex, competition-level math problems would strengthen this category and reflect the increasing application of LLMs in STEM fields.
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