As you work on your Generative AI product, you will likely encounter various Large Language Models and their unique strengths and weaknesses. You'll need to evaluate these models against specific benchmarks to find the right fit for your goals. Multimodal LLM benchmarks help you understand how different models perform on various tasks to determine which will deliver reliable results for your project.
We'll explore the importance of LLM benchmarks, how to read them, and what to consider when integrating them into your evaluation process. You'll also discover how Lamatic's generative AI tech stack solution can help you synthesize benchmarks to choose the right model for your goals.
What are LLM Benchmarks and How They Work
Large Language Model benchmarks are standardized tests designed to measure and compare the abilities of different language models. They consist of:
- Sample data
- Questions or tasks to test LLMs on specific skills
- Metrics for evaluating performance
- Scoring mechanism
With new LLMs released constantly, these benchmarks let researchers and practitioners see how well each model handles different tasks, from basic language skills to complex reasoning and coding.We mainly use LLM benchmarks to establish a consistent, uniform way to evaluate different models. Since LLMs can be used for various use cases, comparing them fairly is difficult. Benchmarks help level the playing field by putting each model through the same set of tests.
The Role of LLM Benchmarks and Evaluating LLM Capabilities
Models are benchmarked based on their capabilities, such as coding, common sense, and reasoning. Other capabilities encompass natural language processing, including:
- Machine translation
- Question answering
- Text summarization
LLM benchmarks play a crucial role in developing and enhancing models. Benchmarks showcase an LLM's progress as it learns, with quantitative measures highlighting where the model excels and its areas for improvement. Guides the fine-tuning process, which helps LLM researchers and developers advance the field. LLM benchmarks also objectively compare different models, helping inform software developers and organizations as they choose which models better suit their needs.
Why Should You Care About LLM Benchmarks?
Evaluation standardization and transparency. LLM benchmarks provide consistent, reproducible ways to assess and rank how well different LLMs handle specific tasks. They allow for an “apples-to-apples” comparison, like grading all students in a class on the same tests.Whenever a new LLM is released, benchmarks help communicate how it compares to others, giving a snapshot of its overall abilities. With shared evaluation standards, others can also independently verify these results using the same tests and metrics.
Progress Tracking and Fine-Tuning
LLM benchmarks also serve as progress markers. You can assess whether new modifications enhance performance by comparing new LLMs with their predecessors. We can already see a history where certain benchmarks became outdated as models consistently surpassed them, pushing researchers to develop more challenging benchmarks to keep up with advanced LLM capabilities.
You can also use benchmarks to identify the model’s weak spots. For instance, a safety benchmark can show how well a given LLM handles novel threats. This, in turn, guides the fine-tuning process and helps LLM researchers advance the field.
Model selection
Benchmarks also provide practitioners with useful references when deciding which model to use in specific applications.You’re building a customer support chatbot powered by an LLM. You’d need a model with strong conversational skills that can:
- Engage in dialogue
- Maintain context
- Provide helpful responses
Which commercial or open-source LLMs should you consider using? By looking at the performance of different models on relevant benchmarks, you can narrow down your shortlist to ones that do well on standard tests.
How Do LLM Benchmarks Work?
LLM benchmarks operate straightforwardly. They supply a task that an LLM must accomplish, evaluate model performance according to a certain metric and produce a score based on that metric. Here’s how each step works in detail:
Setting Up
LLM benchmarks involve preparing sample data coding challenges, large documents, math problems, real-world conversations, and science questions. A range of tasks, including commonsense reasoning, problem-solving, question-answering, summary generation, and translation, are also prepared and given to the model at the outset of testing.
Testing
When running the benchmark, it’s introduced to a model in one of three approaches:
Few-shot
Before prompting an LLM to perform a task, it’s supplied with a few examples showing how to fulfill it. This demonstrates a model’s ability to learn given scarce data.
Zero-shot
An LLM is prompted to complete a task without seeing examples beforehand. This unveils a model’s ability to comprehend new concepts and adapt to novel scenarios.
Fine-tuned
A model is trained on a dataset similar to that used by the benchmark. The goal is to boost the LLM’s command of the task associated with the benchmark and optimize its performance in that specific task.Scoring Once tests are done, an LLM benchmark computes how closely a model’s output resembles the expected solution or standard answer and generates a score between 0 and 100.
Key Metrics for Benchmarking LLMs
Benchmarks apply different metrics to evaluate the performance of LLMs. Here are some common ones:
- Accuracy or precision calculates the percentage of correct predictions.
- Recall, also called the sensitivity rate, quantifies the number of true positives—the actual correct predictions.
- The F1 score blends accuracy and recall into one metric. It considers the two measures equal weight to balance out false positives and false negatives. F1 scores range from 0 to 1, with 1 signifying excellent recall and precision.
- Exact match is the proportion of predictions an LLM matches exactly, and it is a valuable criterion for translation and question answering.
- Perplexity measures how good a model is at prediction. The lower an LLM’s perplexity score, the better it is at comprehending a task.
- Bilingual evaluation understudy (BLEU) evaluates machine translation by computing the matching n-grams (a sequence of n-adjacent text symbols) between an LLM’s predicted and human-produced translations.
- A recall-oriented understudy for gisting evaluation (ROUGE) evaluates text summarization, which has several types. ROUGE-N, for instance, does similar calculations as BLEU for summaries, while ROUGE-L computes the longest common subsequence between the predicted and human-produced summaries.
One or more of these quantitative metrics are usually combined for a more comprehensive and robust assessment.Human evaluation involves qualitative metrics such as coherence, relevance, and semantic meaning. Human assessors examining and scoring an LLM can make for a more nuanced assessment, but it can be labor-intensive, subjective, and time-consuming. Therefore, a balance of both quantitative and qualitative metrics is needed.
What are LLM Leaderboards?
LLM leaderboards publish a ranking of LLMs based on a variety of benchmarks. Leaderboards provide a way to keep track of the myriad LLMs and compare their performance. LLM leaderboards are especially beneficial in making decisions on which models to use.Each benchmark typically has its leaderboard, but independent LLM leaderboards also exist. Hugging Face has a collection of leaderboards, an open LLM leaderboard that ranks multiple open-source models based on the:
- ARC
- HellaSwag
- MMLU
- GSM8K
- TruthfulQA
- Winogrande benchmarks
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25 LLM Evaluation Benchmarks and How They Work
1. Measuring Massive Multitask Language
LLMs Are Getting tested for their knowledge and reasoning abilities across different topics. MMLU is a comprehensive LLM evaluation benchmark created to evaluate large language models' knowledge and reasoning abilities across various topics.
Developed by OpenAI, it’s one of the most extensive benchmarks available. It contains 57 subjects, ranging from general knowledge areas like history and geography to specialized fields like:
- Law
- Medicine
- Computer science
Each subject includes multiple-choice questions at different difficulty levels to assess the model’s understanding of various disciplines.
What is its Purpose?
MMLU aims to test how well a model can generalize across diverse topics and handle a broad array of real-world knowledge, similar to an academic or professional exam. With questions:
- Spanning high school
- Undergraduate
- Professional levels
MMLU evaluates whether a model can accurately respond to complex, subject-specific queries. It is ideal for measuring the depth and breadth of a model’s knowledge.
What Skills Does It Assess?
MMLU assesses several core skills in language models:
- Subject knowledge
- Reasoning and logic
- Adaptability and multitasking
MMLU is designed to comprehensively assess an LLM’s versatility, depth of understanding, and adaptability across subjects, making it an essential benchmark for evaluating models intended for complex, multi-domain applications.
2. Holistic Evaluation of Language Models (HELM)
Developed by Stanford’s Center for Research on Foundation Models, HELM is intended to evaluate models holistically. While other benchmarks test specific skills like:
- Reading comprehension or reasoning
- HELM takes a multi-dimensional approach
- Assessing technical performance and ethical and operational readiness.
What is its Purpose?
HELM aims to move beyond typical language understanding assessments and consider how well models perform across real-world, complex scenarios. By including LLM evaluation metrics for:
- Accuracy
- Fairness
- Efficiency
- And more
HELM aims to create a standard for measuring the overall trustworthiness of language models.
What Skills Does It Assess?
HELM evaluates a diverse set of skills and qualities in language models, including:
- Language understanding and generation
- Fairness and bias mitigation
- Robustness and adaptability
- Transparency and explainability.
HELM is a versatile framework that provides a multidimensional evaluation of language models. It prioritizes not only technical performance but also the ethical and practical readiness of models for deployment in diverse applications.
3. HellaSwag
HellaSwag is a benchmark designed to test commonsense reasoning in large language models. It consists of multiple-choice questions describing a scenario, and the model must select the most plausible continuation among several options. The questions are designed to be challenging, often requiring the model to understand and predict everyday events with subtle contextual cues.
What is its Purpose?
The purpose of HellaSwag is to push LLMs beyond simple language comprehension, testing whether they can reason about everyday scenarios in a way that aligns with human intuition. It’s intended to expose weaknesses in models’ ability to generate or choose answers that seem natural and contextually appropriate, highlighting gaps in their commonsense knowledge.
What Skills Does It Assess?
HellaSwag primarily assesses commonsense reasoning and contextual understanding. The benchmark challenges models to recognize patterns in common situations and select correct and realistic responses. It gauges whether a model can avoid nonsensical answers, an essential skill for generating plausible and relevant text in real-world applications.
4. HumanEval
HumanEval is a benchmark specifically created to evaluate the code-generation capabilities of language models. It comprises programming problems that models solve by writing functional code. Each problem includes input-output examples the generated code must match, allowing evaluators to check if the solutions are correct.
What is its Purpose?
HumanEval measures an LLM’s ability to produce syntactically correct and functionally accurate code. This benchmark focuses on assessing models trained in code generation and is particularly useful for testing models in development environments, where automation of coding tasks can be valuable.
What Skills Does It Assess?
HumanEval assesses programming knowledge, problem-solving ability, and precision in code generation. It checks whether the model can interpret a programming task, apply appropriate syntax and logic, and produce executable code that meets specified requirements. It’s especially useful for evaluating models intended for software development assistance.
5. MATH
MATH is a benchmark specifically designed to test mathematical reasoning and problem-solving skills in LLMs. It consists of various math problems across different topics, including algebra, calculus, geometry, and combinatorics. Each problem requires detailed, multi-step calculations to reach the correct solution.
What is its Purpose?
MATH aims to assess a model’s capacity for advanced mathematical thinking and logical reasoning. It is particularly aimed at understanding if models can solve problems that require more than straightforward memorization or basic arithmetic. MATH provides insight into a model’s ability to handle complex, multi-step operations, which are vital in STEM fields.
What Skills Does It Assess?
MATH evaluates numerical reasoning, logical deduction, and problem-solving skills. Unlike simple calculation tasks, MATH challenges models to break down problems into smaller steps, apply the correct formulas, and logically derive answers. This makes it a strong benchmark for testing models used in scientific, engineering, or educational settings.
6. TruthfulQA
TruthfulQA is a benchmark designed to evaluate how truthful a model’s responses to questions are. It consists of questions that are often intentionally tricky, covering topics where models might be prone to generating confident but inaccurate information (also known as hallucination).
What is its Purpose?
TruthfulQA tests whether models can avoid spreading misinformation or confidently deliver incorrect responses. It highlights models’ tendencies to “hallucinate” and emphasizes the importance of factual accuracy, especially in areas where misinformation can be harmful, like health, law, and finance.
What Skills Does It Assess?
TruthfulQA assesses factual accuracy, resistance to hallucination, and understanding of truthfulness. The benchmark gauges whether a model can distinguish between factual information and plausible-sounding but incorrect content, a critical skill for models used in domains where reliable information is essential.
7. BIG-bench (Beyond the Imitation Game Benchmark)
BIG-bench is an extensive and diverse benchmark designed to test a wide range of language model abilities, from basic language comprehension to complex reasoning and creativity. It includes hundreds of tasks, some unconventional or open-ended, making it one of the most challenging and comprehensive benchmarks available.
What is its Purpose?
BIG-bench aims to push LLMs' boundaries by including tasks beyond conventional benchmarks. It is designed to test models on generalization, creativity, and adaptability, encouraging the development of models capable of handling novel situations and complex instructions.
What Skills Does It Assess?
BIG-bench assesses broad skills, including commonsense reasoning, problem-solving, linguistic creativity, and adaptability. Covering both standard and unique tasks gauges whether a model can perform well across many domains, especially where lateral thinking and flexibility are required.
8. GLUE and SuperGLUE
GLUE (General Language Understanding Evaluation) and SuperGLUE are benchmarks for evaluating basic language understanding skills in LLMs. GLUE includes a series of tasks such as sentence similarity, sentiment analysis, and textual entailment. SuperGLUE is an expanded, more challenging version of GLUE designed for models that perform well on the original GLUE tasks.
What is its Purpose?
GLUE and SuperGLUE aim to provide a standardized measure of general language understanding across foundational NLP tasks. These benchmarks aim to ensure that models can handle common language tasks essential for general-purpose applications, establishing a baseline for linguistic competence.
What Skills Does It Assess?
GLUE and SuperGLUE assess language comprehension, sentiment recognition, and inference skills. They measure whether models can interpret sentence relationships, analyze tone, and understand linguistic nuances. These benchmarks are fundamental for evaluating models intended for conversational AI, text analysis, and other general NLP tasks.
9. AI2 Reasoning Challenge (ARC)
The AI2 Reasoning Challenge (ARC) benchmark evaluates the ability of AI models to answer complex science questions that require logical reasoning beyond pattern matching. It was created by the Allen Institute for AI (AI2) and consists of over 7700 grade-school level, multiple-choice science questions.
The dataset is split into an easy set and a challenge set. Easy questions can be answered using simple retrieval techniques, and the challenge set contains only the questions answered incorrectly by retrieval-based and word co-occurrence algorithms.
10. Chatbot Arena
Chatbot Arena is an open benchmark platform that pits two anonymous chatbots against each other. Users have random real-world conversations with both chatbots in an “arena,” and then cast votes on which one they prefer, after which the models’ identities are revealed.
This crowdsourced pairwise comparison data is fed into statistical methods that estimate scores and create approximate rankings for various LLMs. Sampling algorithms are also used to pair models.
11. Grade School Math 8K (GSM8K)
GSM8K tests an LLM’s mathematical reasoning skills. It has a corpus of 8,500 grade-school math word problems. Solutions are collected in natural language instead of mathematical expressions. AI verifiers are trained to evaluate model solutions.
12. Winogrande
Winogrande evaluates an LLM’s commonsense reasoning capabilities. It builds upon the original Winograd Schema Challenge (WSC) benchmark, with a huge dataset of 44,000 crowdsourced problems using adversarial filtering. Scoring is based on accuracy.
13. SWE-bench
Like HumanEval, SWE-bench tests an LLM’s code generation skills, focusing on issue resolution. Models are tasked with fixing bugs or addressing feature requests in a specific code base. The benchmark’s assessment metric is the percentage of resolved task instances.
14. MT-Bench
The researchers behind Chatbot Arena also created MT-Bench, which is designed to test how well an LLM can engage in dialogue and follow instructions. Its dataset consists of open-ended multi-turn questions, with 10 questions each in these eight areas:
- Coding
- Extraction
- knowledge I (STEM)
- knowledge II (humanities and social sciences)
- Math
- Reasoning
- Roleplay
- Writing
15. SQuAD (GitHub):
The Stanford Question Answering Dataset (SQuAD) tests reading comprehension. The benchmark contains 107,785 question-answer pairs on 536 Wikipedia articles written by humans through crowdsourcing.
SQuAD 2.0 also contains 50,000 unanswered questions to test whether models can determine when the source material supports no answer and opt not to answer. A separate test set is kept private so as not to compromise the integrity of the results (e.g. by letting models be trained on them). To get your model evaluated on the SQuAD test set, you just need to submit it to the benchmark’s developers.
16. MuSR (GitHub)
MuSR stands for Multi-step Soft Reasoning. The dataset is designed to evaluate models of commonsense chain-of-thought reasoning tasks in natural language. MuSR has two main characteristics that differentiate it from other benchmarks:
- Algorithmically generated datasets with complex problems
- The dataset contains free-text narratives that correspond to real-world reasoning domains.
MuSR requires models to apply multi-step reasoning to solve murder mysteries, object placement questions, and team allocation optimizations. Models have to parse long texts to understand context and then apply reasoning based on that context. MuSR is part of the Open LLM Leaderboard by Hugging Face.
17. GPQA (GitHub)
GPQA stands for Graduate-Level Google-Proof Q&A Benchmark. It’s a challenging dataset of 448 multiple-choice questions spanning the domains of:
- Biology
- Physics
- Chemistry
The questions in GPQA can be considered very difficult. Experts, including those with Ph. D.s, can only achieve about 65% accuracy in answering these questions. Questions are actually hard enough to be Google-proof. For example, even with free web access and 30+ minutes of researching a topic, out-of-domain validators (e.g., a biologist answering a chemistry question) could only achieve 34% accuracy. GPQA is part of the Open LLM Leaderboard by Hugging Face.
18. MedQA (GitHub
The abbreviation stands for Medical Question Answering benchmark. It’s a multiple-choice question-answering evaluation based on United States Medical License Exams. This benchmark covers three languages with tons of questions:
- English (12k+ questions)
- Simplified Chinese (34k+ questions)
- Traditional Chinese (14k+ questions)
19. PyRIT
PyRIT stands for Python Risk Identification Tool for Generative AI (PyRIT). It’s more of a framework than a standalone benchmark, but a useful tool developed by Microsoft. PyRIT is a tool to evaluate LLM robustness against a range of harm categories.
It can be used to identify harm categories, including fabricated/ungrounded content (for instance, hallucination), misuse (bias, malware generation, jailbreaking), prohibited content (such as harassment), and privacy harms (identity theft). The tool automates red teaming tasks for foundation models, and thus aims to contribute to securing the future of AI.
20. Purple Llama CyberSecEval (GitHub)
CyberSecEval (a product of Meta’s Purple Llama project focuses on the cybersecurity of models used in coding. It claims to be the most extensive unified cybersecurity safety benchmark. CyberSecEval covers two crucial security domains:
- The likelihood of generating insecure code compliance when prompted to help with cyberattacks. It can be used to assess how much LLMs are willing and able to assist cyber attackers, safeguarding against misuse.
- CyberSecEval provides metrics for quantifying the cybersecurity risks associated with LLM-generated code. CyberSecEval 2 is an improvement to the original benchmark, extending the evaluation to prompt injection and code interpreter abuse.
21. Mostly Basic Programming Problems (MBPP)
Mostly Basic Programming Problems (MBPP) measures LLMs' ability to synthesize short Python programs from natural language descriptions. The dataset contains 974 tasks for entry-level programmers focusing on common programming concepts such as:
- List manipulation
- String operations
- Loops
- Conditionals
- Basic algorithms
Each problem contains a task description, an example code solution, and test cases to verify the LLM's output.
22. Berkeley Function Calling Leaderboard
The Berkeley Function-Calling Leaderboard (BFCL) is designed to evaluate the function-calling capabilities of different LLMs thoroughly. It features 2,000 question-function-answer pairs across various languages and application domains with complex use cases. The BFCL also tests function relevance detection, determining how models handle unsuitable functions.
Key features of BFCL include:
- 100 Java
- 50 JavaScript
- 70 REST API
- 100 SQL
- 1,680 Python cases
Scenarios involving simple, parallel, and multiple-function calls. Function relevance detection to ensure appropriate function selection. They have also created a visualization of the outcomes to help understand this data.
23. MetaTool Benchmark
MetaTool is a benchmark to assess whether LLMs possess tool usage awareness and can correctly choose tools. It includes the ToolE Dataset, which contains prompts triggering single-tool and multi-tool scenarios and evaluates tool selection across four subtasks. Results from experiments on nine LLMs show that most still face challenges in effective tool selection, revealing gaps in their intelligence capabilities.
24. FinBen
FinBen is an open-source benchmark designed to evaluate LLMs in the financial domain. It includes 36 datasets that cover 24 tasks in seven financial domains:
- Information extraction
- Text analysis
- Question answering
- Text generation
- Risk management
- Forecasting
- Decision-making
FinBen offers a broader range of tasks and datasets compared to its predecessors and is the first to evaluate stock trading. The benchmark revealed that while the latest models excel in information extraction and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting.
25. LegalBench
LegalBench is a collaborative benchmark designed to evaluate the legal reasoning abilities of LLMs. It consists of 162 tasks, which legal professionals crowdsource. These tasks cover six different types of legal reasoning:
- Issue-spotting
- Rule-recall
- Rule-application
- Rule-conclusion
- Interpretation
- Rhetorical understanding
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8 Limitations of LLM Benchmarks
1. Data Contamination: Why It Matters
Public test data can unintentionally leak into datasets used to train LLMs, compromising evaluation integrity. If a model has seen specific answers during training, it may “know” them rather than demonstrate a true ability to solve that task. One way to prevent this is to keep some benchmark data private and regularly create new or expand benchmark datasets.
2. Benchmarks Can Quickly Become Outdated
Once a model achieves the highest possible score on a particular benchmark, that benchmark loses its effectiveness as a measure of progress. This necessitates the creation of more complex and nuanced tasks to keep pushing the boundaries of LLM development. Many of the existing benchmarks already lost their relevance as modern LLMs progress in their abilities.
3. Benchmarks May Not Reflect Real-World Performance
Many benchmarks are built around specific, well-defined tasks that may not fully capture the complexity and variety of scenarios encountered in real-world applications. A model that excels in benchmarks may still need to improve on applied tasks, even those that seem straightforward.
4. Bounded Scoring
Once a model reaches the highest possible score for a certain benchmark, that benchmark will need to be updated with more complex tasks to make it a useful measure.
5. Broad Dataset
Since LLM benchmarks use sample data derived mostly from a broad range of subjects and a wide array of tasks, they may not be a fitting metric for edge scenarios, specialized areas or specific use cases.
6. Finite Assessments
LLM benchmarks can only test a model’s current skills. However, new benchmarks must be created as LLMs advance and novel capabilities emerge.
7. Overfitting
If an LLM is trained on the same dataset as the benchmark, it could lead to overfitting, wherein the model might perform well on the test data but not on real-world data. This results in a score that doesn’t reflect an LLM’s abilities.
8. Benchmarks Aren’t Enough for Evaluating LLM Apps
Generic LLM benchmarks are useful for testing models but don’t work for LLM-powered applications. In real apps like chatbots or virtual assistants, it’s not just the model. You also have prompts, external knowledge databases, and business logic to consider. To test these systems effectively, you’ll need “your own” benchmarks, including real, application-specific inputs and standards for correct behavior.
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