Unpacking Language Model Benchmarks: A Guide (Part 1)
Exploring the tests that shape AI capabilities from coding prowess to common sense mastery
How do we know if an AI is truly intelligent? As large language models (LLMs) grow increasingly sophisticated, tackling everything from coding to creative writing, this question becomes more pressing than ever. Enter the world of AI benchmarks: the yardsticks by which we measure machine intelligence.
Language models, the powerhouses behind tools like ChatGPT, Mistral’s Codestral and Anthropic’s Claude, have transformed how we interact with technology. But with great power comes great responsibility—and great curiosity. How well can these models really code? Do they understand the world the way we do? Benchmarks are our window into these questions, offering standardized tests that push AI to its limits.
In this series, we'll dive deep into the landscape of language model benchmarks, demystifying how researchers quantify the capabilities of these artificial minds. Part 1 focuses on two critical domains: coding and common-sense reasoning. We'll unpack benchmarks that evaluate an AI's ability to write programs and those that test its grasp of everyday logic—skills that are second nature to humans but profoundly challenging for machines.
From the intricacies of the HumanEval benchmark, where models face real-world programming challenges, to the nuanced scenarios of the WinoGrade where common sense is put to the test, we'll explore what these benchmarks measure and why their insights matter for the future of AI development.
So, let's begin our journey into the heart of AI evaluation, starting with the benchmarks that assess a language model's coding prowess...
HumanEval benchmark assesses the ability of language models to generate functionally correct code snippets based on natural language descriptions of programming tasks. The primary metric is the "pass@k" score, which measures the percentage of problems that the model can solve correctly when given k attempts. For example, a pass@10 score of 50% means that the model can solve 50% of the problems correctly when given 10 attempts.
MBPP (Mostly Basic Programming Problems) aims to evaluate how well language models can generate functional and correct code for a variety of basic programming problems based on natural language descriptions.
RepoBench is a benchmark designed for evaluating repository-level code auto-completion systems. It supports both Python and Java and consists of three interconnected evaluation tasks: RepoBench-R (Retrieval), RepoBench-C (Code Completion), and RepoBench-P (Pipeline).
Spider, Text-to-SQL benchmark dataset, contains both databases with multiple tables in different domains and complex SQL queries. It was designed to test the ability of a system to generalize to not only new SQL queries and database schemas but also new domains.
Code may be the language of computers, but common sense is the unspoken language of human interaction. As we transition from coding benchmarks to reasoning benchmarks, we'll see how researchers assess an AI's fluency in this crucial yet elusive skill.
ARC (AI2 Reasoning Challenge) is a benchmark for evaluating models on a collection of 7787 multiple-choice science questions, authored for use on standardized tests. It consists of two subtasks:
ARC-Easy (5197 questions): This task involves answering multiple-choice questions that require basic common-sense reasoning.
ARC-Challenge (2590 questions): This task involves answering nuanced questions that require deeper understanding and reasoning.
BoolQ (Boolean Questions) is a task that evaluates the ability to answer naturally occurring yes/no questions based on the context of a given passage. The model is given a passage and a boolean question. The model must determine whether the statement in the question is true or false based on the passage.
HellaSwag is a benchmark for common sense natural language inference (NLI). It consists of 70k multiple choice questions from ActivityNet or WikiHow - with four answer choices about what might happen next in the scene. For each question, a model is given a context from a video caption or a how-to article and four ending choices for what might happen next. Only one choice is right – the actual next caption of the video.
PIQA (Physical Interaction: Question Answering) is a task that evaluates models on their knowledge of physical common sense ranging from cooking to car repair. It assesses whether language representations capture knowledge traditionally only seen or experienced. The underlying task is multiple choice question answering: given a question q and two possible solutions s1, s2, a model or a human must choose the most appropriate solution, of which exactly one is correct.
SciQ is a benchmark that evaluates the ability to answer scientific questions. It is a dataset of 13.7K multiple choice science exam questions. The benchmark dataset supports two tasks.
Multiple-choice: To determine the most accurate answer based on a given question and answer choices.
Direct Answer: To identify and extract the exact text segment that contains the answer.
WinoGrande is a benchmark to evaluate the models on common sense capabilities. A dataset of 44k problems assesses whether these models genuinely possess robust common sense or merely exploit dataset biases that overestimate their true capabilities. It is formatted as a fill-in-the-blank problem where the blank corresponds to the mention of one of the two names in the context.
The trend in LLM evaluation is moving towards more dynamic benchmarks that can adapt to the model's capabilities, generate adversarial examples, or involve interactive components. This shift aims to provide a more robust assessment of LLMs' true capabilities and to mitigate the effects of models potentially memorizing static datasets.
Dynamic benchmarks often try to assess not just the model's ability to produce correct answers, but also its reasoning process, adaptability, and generalization to novel situations. They may also incorporate human feedback or pairwise comparisons to evaluate subjective qualities like helpfulness or preference.
However, static benchmarks still play a crucial role in providing standardized, reproducible evaluations and tracking progress over time. They allow for direct comparisons between different models and versions, which is harder with fully dynamic benchmarks.
As LLMs continue to advance, we can expect to see more sophisticated dynamic benchmarks that combine elements of interaction, adaptation, and even multi-modal inputs to push the boundaries of AI evaluation.