We have all seen the headlines: “This is the most powerful LLM yet, and it beats all other large language models (LLMs) on these benchmarks” (i.e., model evaluation metrics). And then another LLM evaluation process gives the crown to a different LLM system based on a different set of model evaluation metrics. Additionally, all major tech companies investing in the development of more capable models are marketing their models as superior to those of their competitors.
This makes finding the best LLM for a particular use case quite tough. Hence, this guide will explain how to perform a thorough LLM evaluation and highlight the main benchmarks across various domains, including text generation, code generation, reasoning, and image and video processing, as well as multimodal capabilities.
LLM evaluation metrics—the what
Large language models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities across a wide range of tasks. However, as these language models become increasingly prevalent and influential, there is a need for objective LLM evaluation metrics, performance evaluation criteria, and methods for evaluating large language models (LLMs). Human evaluation is both time consuming and ultimately subjective, so evaluation tools, benchmarks, and evaluation metrics serve as essential tools for assessing and comparing the performance of different language models, providing valuable insights into their strengths, weaknesses, and overall effectiveness.
At their core, LLM system evaluation benchmarks are standardized tests designed to measure various aspects of a model’s linguistic and cognitive abilities. These benchmarks typically consist of diverse evaluation datasets and task sets, ranging from basic language understanding to complex reasoning and knowledge application. By subjecting different models to the same set of challenges, researchers and developers can more objectively evaluate their performance and track progress in the field.
LLM evaluation metrics, on the other hand, are specific measurements used to quantify a model’s performance against these benchmarks. These metrics can include accuracy, perplexity, BLEU scores for machine translation tasks, or more specialized measures tailored to specific applications. By providing numerical representations of a model’s performance, these metrics enable precise comparisons between models and facilitate model training and the identification of areas for improvement.
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LLM evaluation metrics—the why
LLM benchmarks and evaluation metrics play a crucial role in guiding research and development efforts, helping to identify promising approaches and areas that require further attention. Moreover, they provide a common language for discussing and comparing models, which is needed for both collaboration and competition within the AI community. For end users and organizations looking to adopt large language models, these benchmarks offer valuable information for making informed decisions about which models best suit their needs.
Although not designed for this purpose, LLM evaluation metrics are also used as a marketing tool for language model developers, which can lead to misleading claims about the superiority of some language models over others. Furthermore, as LLMs continue to advance, the AI community faces ongoing challenges in developing benchmarks that can keep pace with rapidly evolving models, capture the nuances of human-like language understanding, and address concerns about bias detection and fairness.
It is also important to remember that although the latest models like GPT 4, GPT4o, Gemini, or Claude are still largely text-based, they are increasingly integrating multimodal features, making them capable of analyzing images, sounds, and video. These models are also capable of tackling complex reasoning and problem-solving tasks that resemble cognitive processes, though they do not yet exhibit anything close to human-like intelligence.
Thus, the model evaluation process for these large language models needs to include evaluation metrics for all these aspects. By understanding the different benchmarks, LLM evaluation methods, and metrics, we can make informed decisions about which models to choose for performance assessment in a given application.
Text-centered LLM evaluation metrics
Text-centered LLM performance evaluation metrics play a crucial role in assessing the performance and capabilities of large language models. These metrics provide standardized benchmarks against which to measure various aspects of language understanding, reasoning, and generation.
- MMLU (Massive Multitask Language Understanding): This benchmark covers a wide range of subjects, testing a model’s ability to answer multiple-choice questions across 57 different fields, assessing both breadth and depth of knowledge.
- HellaSwag: Focused on common-sense reasoning, HellaSwag presents models with multiple-choice questions about everyday scenarios. It challenges LLMs to demonstrate understanding of implicit knowledge and logical inference in real-world contexts.
- DROP (Discrete Reasoning Over Paragraphs): This dataset requires models to not only extract information from text but also perform numerical reasoning, testing their ability to integrate different types of cognitive tasks.
- GLUE (General Language Understanding Evaluation) is a collection of nine diverse natural language understanding tasks, including sentiment analysis, textual entailment, and question answering.
- SuperGLUE: Building upon the former LLM evaluation tool, SuperGLUE introduces more challenging NLP tasks designed to push the boundaries of language models. It includes nuanced natural language inference and tasks requiring understanding of coreference and causal relationships.
- SQuAD (Stanford Question Answering Dataset) presents language models with paragraphs from Wikipedia and associated questions, testing their ability to understand context, extract relevant information, and formulate accurate answers based on a given text.
Evaluating LLMs on their reasoning ability
LLM reasoning tests are designed to evaluate models on their ability to think logically, solve problems, and apply knowledge in complex scenarios. These benchmarks go beyond simple language understanding, challenging models to demonstrate higher-order cognitive skills akin to human reasoning.
- BIG-Bench-Hard: A curated selection of challenging tasks from the broader BIG-Bench suite, presented as chain-of-thought problems. It tests a model’s ability to break down complex reasoning into step-by-step thought processes.
- GSM8K: Focuses on grade-school math problems, assessing a model’s mathematical reasoning and problem-solving skills. It requires an understanding of word problems and application of basic arithmetic operations.
- MATH: Covers a wide range of mathematical disciplines across varying difficulty levels. It evaluates advanced mathematical reasoning, from basic algebra to complex calculus and beyond.
- MathVista: A comprehensive multimodal reasoning benchmark combining mathematical problem-solving with visual interpretation. It tests language models’ ability to integrate information from text and images to solve complex problems.
- ARC: Presents grade-school science questions that require reasoning beyond simple fact retrieval. It assesses a model’s ability to apply scientific concepts and logical thinking to arrive at correct answers.
- CommonsenseQA: Evaluates a model’s grasp of common-sense knowledge and reasoning through multiple-choice questions. It tests the ability to make inferences based on everyday understanding of the world.
- OpenBookQA: Requires models to combine given information with broader knowledge to answer questions. It assesses large language models’ abilities to integrate explicit and implicit knowledge in reasoning tasks.
- Winograd Schema Challenge: Tests large language models’ capacities for common-sense reasoning in resolving ambiguous pronoun references. It evaluates nuanced language understanding and contextual inference skills.
LLM evaluation on code writing and programming
Code and programming LLM evaluation metrics measure large language models’ abilities to understand, generate, and manipulate code across various programming languages and tasks. These tests evaluate not just syntax but also problem-solving skills, algorithm design, and the capacity to translate natural language requirements into functional code.
- HumanEval: This collection of Python coding tasks is designed to test large language models’ code generation capabilities and evaluate their ability to produce correct, efficient, and human-like (hence “human evaluation” in the name) code solutions to given problems.
- APPS: This benchmark focuses on Python code generation without relying on web-scraped content and encompasses a wide range of coding challenges, from beginner to advanced levels, comprehensively assessing large language models’ programming abilities.
- MBPP: Another diverse set of Python programming problems, which test both code generation and understanding. LLM evaluation criteria in MBPP focus on a model’s capacity to interpret problem statements and produce appropriate code solutions across various difficulty levels.
- CodeXGLUE: This comprehensive evaluation framework, covering multiple programming languages and tasks, includes challenges such as code completion, bug fixing, code translation between languages, and code refinement, holistically evaluating LLMs’ coding capabilities.
Evaluating model capabilities for image processing
Image-based evaluation metrics for LLM system evaluation assess a models’ ability to understand, interpret, and reason about visual information in conjunction with text. These benchmarks test the integration of visual and linguistic processing, challenging models to perform tasks that require a deep understanding of both modalities.
- VQAv2: This LLM evaluation tool measures a model’s capacity to answer questions about natural images. It tests its ability to interpret visual content and respond to diverse queries about objects, actions, and relationships depicted in photographs.
- TextVQA: Focuses on a model’s performance in reading and understanding text within natural images. It assesses skills in optical character recognition, text comprehension, and integration of textual and visual information.
- DocVQA: Challenges models to understand and extract information from document images. It tests abilities in document layout analysis, text extraction, and comprehension of structured information in various document formats.
- ChartQA: Assesses a model’s capability to interpret and answer questions about charts and graphs. It includes LLM evaluation in the areas of data visualization comprehension, numerical reasoning, and extraction of insights from graphical representations.
- InfographicVQA: Tests large language models’ abilities to understand and answer questions about complex infographics, including processing multimodal information, understanding design elements, and extracting key information from visually rich representations.
Video-based LLM evaluation metrics
Video-based LLM evaluation metrics assess a model’s ability to understand and reason about dynamic visual content combined with audio and text. These benchmarks test the integration of multiple modalities over time, challenging models to comprehend complex spatiotemporal relationships and narrative structures in video content.
- VATEX: A bilingual video captioning dataset that tests a model’s ability to generate accurate descriptive captions in both English and Chinese. It evaluates cross-language understanding and a model’s capacity to summarize video content accurately in different languages.
- YouCook2: Focuses on captioning instructional cooking videos in English. It assesses a model’s ability to understand and describe sequential actions, ingredients, and cooking processes in a specific domain.
- NextQA: A video question-answering evaluation process that requires models to reason about temporal relationships and predict future events. It tests their ability to understand video content and make logical inferences based on observed actions and contexts.
- ActivityNet-QA: Presents question-answering tasks over a diverse range of human activities in videos. It evaluates a model’s comprehension of complex actions, event sequences, and contextual understanding in long-form video content.
- Perception Test MCQA: This multiple-choice question-answering LLM evaluation framework based on video content assesses a model’s ability to perceive and interpret visual and auditory information in videos, testing both factual recall and inferential reasoning about the content.
Evaluation frameworks for audio processing
Audio-based evaluation metrics for LLMs assess the models’ abilities to process and understand spoken language across various contexts and languages. These benchmarks test skills in speech recognition, language identification, comparative analysis, and translation, challenging models to bridge the gap between auditory input and textual understanding.
- FLEURS: A multilingual automatic speech recognition (ASR) benchmark that evaluates a model’s ability to transcribe speech across numerous languages. It tests the model’s capacity to handle diverse accents, speaking styles, and linguistic structures in a global context.
- VoxPopuli: Another multilingual ASR benchmark, focusing on transcribing speech from various languages in political contexts. It assesses a model’s ability to handle domain-specific vocabulary and speech patterns across different languages and dialects.
- Multilingual Librispeech: A collection of ASR tasks based on audiobooks in multiple languages. It evaluates a model’s proficiency in transcribing clear, articulate speech across different linguistic environments, testing both accuracy and language adaptability.
- CoVoST 2: A speech translation benchmark that challenges models to translate spoken content from various source languages into English. It assesses not only speech recognition capabilities but also the ability to accurately convey meaning across languages in spoken form.
MMMU (massive multi-discipline multimodal understanding and reasoning)
This multimodal LLM evaluation benchmark is designed to test models on a wide range of tasks that require college-level subject knowledge and deliberate reasoning. It includes 11.5K multimodal questions collected from college exams, quizzes, and textbooks, covering six core disciplines: art & design, business, science, health & medicine, humanities & social science, and tech & engineering.
The questions span 30 subjects and 183 subfields, including 32 heterogeneous image and diverse reference data types, such as charts, diagrams, maps, tables, sheet music, and chemical structures. The benchmark focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts.
X-shot approaches in LLM evaluation
In LLM evaluation, terms like “0-shot,” “3-shot,” “4-shot,” or “x-shot” refer to the number of examples provided to the model before it performs a task. Zero-shot (0-shot) means that the model receives no examples and must rely on its pre-existing knowledge. Few-shot approaches, such as 3-shot or 4-shot, involve giving the model a few examples (three or four, respectively) to guide its task performance. The term x-shot is a general reference to any specified number of examples provided. These different LLM evaluation frameworks and evaluation methods assess how well a model can handle unfamiliar tasks using either preexisting knowledge (zero-shot) or a few examples (few-shot).
LLM evaluations—conclusions
LLM model evaluations and evaluation benchmarks are indispensable tools for measuring the capabilities and performance of large language models (LLMs) across a variety of tasks, from text processing to multimodal reasoning. As these models evolve into more advanced systems capable of handling text, images, code, and even video and audio, the importance of well-rounded, comprehensive evaluation frameworks has grown. Although LLM model evaluation test data can lead to somewhat misleading marketing messages, by understanding the diverse benchmarks and metrics used in LLM model evaluations, stakeholders can make more informed decisions about which models best align with their goals.
