How to Build an LLM Evaluation Framework & Top 20 Performance Benchmarks

Large language models have quickly become valuable tools for automating tasks across numerous industries. Evaluating the performance of these models can be challenging. How can you determine if a model is reliable enough for your specific use case? What metric should you use to measure performance? How can you ensure the model is producing fair and unbiased outputs? These questions and more highlight the importance of an LLM evaluation framework. In this article, we'll explore the ins and outs of multimodal LLM evaluation frameworks, including how to build one, and the best practices for creating a robust framework that can help you assess models for your unique tasks.  A reliable LLM evaluation framework will help you test for and apply performance benchmarks to optimize your model’s accuracy, fairness, and effectiveness.

Lamatic's AI tech stack is a valuable tool to help you achieve your objectives. It can help you build a robust LLM evaluation framework that simplifies the process of testing language models and assessing their performance against your benchmarks. 

What is LLM Evaluation and Its Importance

LLM evaluation is the process of testing and measuring how well large language models perform in real-world situations. When we test these models, we look at how well they understand and respond to questions, how smoothly they generate text, and whether their responses make sense in context. This step is super important because it helps us catch any issues and improve the model, ensuring it can handle tasks effectively and reliably before they go live.

Why Do You Need to Evaluate an LLM?

Make sure the model meets the task and its requirements. Evaluating an LLM ensures it understands and responds accurately, handles different types of information correctly, and communicates safely, clearly, and effectively. 

This step is essential because it allows us to fine-tune the model based on real feedback, improving its performance and reliability. By doing thorough evaluations, we ensure the LLM can meet the needs of its users, whether it's answering questions, providing recommendations, or creating content.

Example Use Case in Customer Support

Let's say you're using an LLM in customer support for an online retail store. Here's how you might evaluate it: 

• Start by setting up the LLM to answer common customer inquiries like order status, product details, and return policies. 
• You'd run simulations using a variety of real customer questions to see how the LLM handles them. For example, you might ask, "What's the return policy for an opened item?" or "Can I change the delivery address after placing an order?" 
• During the evaluation, you'd check if the LLM's responses are accurate, clear, and helpful. 
• Does it fully understand the questions?
• Does it provide complete and correct information? 
• If a customer asks something complex or ambiguous, does the LLM ask clarifying questions or jump to conclusions? 
• Does it produce toxic or harmful responses? 

You're also building a valuable dataset as you collect data from these simulations. You can then use this data for LLM fine-tuning and RLHF to improve the model's performance. This constant testing, gathering data, and improving cycle helps the model work better. It ensures the model can reliably help real customers, improving their experience and making things more efficient.

The Importance of Custom LLM Evaluations

Custom evaluations are key because they ensure models match customers' needs. You start by figuring out the industry's unique challenges and goals. Create test scenarios that mirror the real tasks the model will face, whether answering customer service questions, analyzing data, or writing content that strikes the right chord. 

You must also ensure your models can responsibly handle sensitive topics like toxicity and harmful content. This is crucial for keeping interactions safe and positive. This approach doesn't just check if a model works well overall; it checks if it works well for its specific job in a real business setting. This is how you ensure your models help customers reach their goals.

LLM Model Evaluations vs. LLM System Evaluations

When we talk about evaluating large language models, it's important to understand there's a difference between looking at a standalone LLM and checking the performance of a whole system that uses an LLM. Modern LLMs are pretty strong, handling a variety of tasks like:

• Chatbots
• Recognizing named entities (NER)
• Generating text
• Summarizing
• Answering questions
• Analyzing sentiments
• Translating and more

These models are often tested against standard benchmarks like:

• GLUE
• SuperGLUE
• HellaSwag
• TruthfulQA
• MML

Using well-known metrics. However, these LLMs may not fit your specific needs straight out of the box. Sometimes, we must fine-tune the LLM with a unique dataset for our particular application. 

Evaluating these adjusted models or models that use techniques like Retrieval augmented generation (RAG) usually means comparing them to a known, accurate dataset to see how they perform. But remember, ensuring that an LLM works as expected isn't just about the model itself but also how we set things up. This includes choosing the right prompt templates, setting up efficient data retrieval systems, and tweaking the model architecture if necessary. Although picking the right components and evaluating the entire system can be complex, ensuring the LLM delivers the desired results is crucial.

Related Reading

• LLM Security Risks
• What is an LLM Agent
• AI in Retail
• LLM Deployment
• How to Run LLM Locally
• How to Use LLM
• LLM Model Comparison
• AI-Powered Personalization
• How to Train Your Own LLM

How to Build an LLM Evaluation Framework, from Scratch

Setting Up Your LLM Evaluation Framework – Where to Start? 

Creating a robust LLM evaluation framework isn’t easy, so it took me over five months to build DeepEval, an open-source LLM evaluation framework. From working closely with hundreds of open-source users, you can tell that the first step to building an LLM evaluation framework is to set up the infrastructure needed to make a mediocre LLM evaluation framework great. There are two components to any evaluation or testing framework: 

• The thing to be evaluated (the “evaluatee”)
• The thing we’re using to assess the evaluatee (the “evaluator”)

In this case, the “evaluatee” is an LLM test case containing the information for the LLM evaluation metrics, the “evaluator,” to score your LLM system. To be more concrete, an LLM test case should contain parameters such as: 

• Input: The input to your LLM system. Note that this does NOT include the prompt template but the user input (we’ll see why later). 
• Actual Output: The actual output of your LLM system for a given input. We call it “actual” output because… 
• Expected Output: The expected output of your LLM system for a given input. This is where data labelers, for example, would give target answers for a given input. 
• Context: The undisputed ground truth for a given input. Context and expected output are often time confused with one another since they are both factually similar. An excellent example to clarify things is that context could be a raw PDF page containing everything you need to answer a question in the input, but the expected output is how you would want the answer answered. 
• Retrieval Context: The retrieved text chunks in a RAG system. As the description suggests, this is only applicable to RAG applications. 

Note that only the input and actual output parameters are mandatory for an LLM test case. This is because some LLM systems might just be an LLM itself, while others can be RAG pipelines that require parameters such as retrieval context for evaluation. The point is that different LLM architectures have different needs but are generally similar. Here’s how you can set an LLM test case in code:

Implementing LLM Evaluation Metrics Requires Careful Consideration

We need to implement LLM evaluation metrics into our framework. This is probably the toughest part of building an LLM evaluation framework, which is also why you’ve dedicated an entire article discussing everything you need to know about LLM evaluation metrics. 

Contextual Relevance is probably the simplest RAG retrieval metric, and you’ll notice it unsurprisingly overlooks important factors such as the positioning/ranking of nodes. This is important because more relevant nodes should be ranked higher in the retrieval context, as it greatly affects the quality of the final output. This is calculated using another metric called contextual precision.

Generate LLM Test Cases with Synthetic Data

After implementing LLM evaluation metrics into your framework, the next step is to create or generate LLM test cases. You might have your own set of test cases for your specific domain, but chances are you’ll need more than you have to evaluate your LLM system adequately. And while you can create test cases manually, it’s laborious and time-consuming. You can use LLMs to generate synthetic data that will help you evaluate your LLM system.  

The Benefits of Using Synthetic Data

Although this step is optional, you’ll likely find generating synthetic data more accessible than creating your own LLM test cases/evaluation datasets. That’s not to say generating synthetic data is a straightforward thing to do  . LLM-generated data can sound and look repetitive and might not represent the underlying data distribution accurately, which is why, in DeepEval, we had to evolve or complicate the generated synthetic data multiple times.

Considerations for Synthetic Data Generation

If you're interested in learning more about synthetic data generation, here is an article you should read. Generating synthetic data generates input-(expected)output pairs based on some given context. However, you recommend avoiding using “mediocre” (i.e., non-OpenAI or Anthropic) LLMs to generate expected outputs since they may introduce hallucinated expected outputs in your dataset. 

We can create an EvaluationDataset class:

from typing import List
class EvaluationDataset:
def init(self, test_cases: List[LLMTestCase] = None):
# Initialize the dataset with optional test cases
self.test_cases = test_cases if test_cases else []

def generate_synthetic_test_cases(self, contexts: List[List[str]]):
    """
    Generate synthetic test cases using an LLM to create input-output pairs based on contexts.
    """
    for context in contexts:
        # generate_input_output_pair() is assumed to generate input and expected output
        input, expected_output = generate_input_output_pair(context)
        test_case = LLMTestCase(
            input=input,
            expected_output=expected_output,
            context=context
        )
        self.test_cases.append(test_case)

def evaluate(self, metric):
    """
    Evaluate all test cases using the provided metric.
    """
    for test_case in self.test_cases:
        metric.measure(test_case)
        print(test_case, metric.score)

###################
## Example Usage ##
###################

# Define a contextual relevancy metric instance
metric = ContextualRelevancyMetric()

# Create an EvaluationDataset instance
dataset = EvaluationDataset()

# Generate synthetic test cases from given contexts
dataset.generate_synthetic_test_cases([["..."], ["..."]])

# Evaluate the dataset using the provided metric
dataset.evaluate(metric)

The LLM generation part out since you’ll likely have unique prompt templates depending on the LLM you’re using, but if you’re looking for something quick you can borrow DeepEval’s synthetic data generator, which you can pass in entire documents instead of lists of strings that you have to process yourself: pip install deepeval from deepeval.dataset import EvaluationDataset dataset = EvaluationDataset() dataset.generate_goldens_from_docs( document_paths=['example_1.txt', 'example_2.docx', 'example_3.pdf'], max_goldens_per-document= 2 ) For the sake of simplicity, “goldens” and “test cases” can be interpreted as the same thing here. Still, the only difference being goldens are not instantly ready for evaluation (since they don't have actual outputs).

Speed Up Your LLM Evaluation Framework

You’ll notice that in the evaluate() method, we used a for loop to evaluate each test case. This can get very slow as it is not uncommon for there to be thousands of test cases in your evaluation dataset. What you’ll need to do, is to make each metric run asynchronously, so the for loop can execute concurrently on all test cases, at the same time. But beware, asynchronous programming can get very messy especially in environments where an event loop is already running (eg. colab/jupyter notebook environments), so it is vital to handle asynchronous errors gracefully. Going back to the contextual relevancy metric implementation to make it asynchronous: import asyncio class ContextualPrecisionMetric:

# ...previous methods ########################
### New Async Method ###
########################

async def a_measure(self, test_case: LLMTestCase):
irrelevant_count = 0
relevant_count = 0
tasks = [] # Initialize the tasks list


# Prepare tasks for asynchronous execution
for node in test_case.retrieval_context:
    # Here, is_relevant is assumed to be async
    task = asyncio.create_task(is_relevant(node, test_case.input))
    tasks.append(task)

# Await the tasks and process results as they come in
for task in asyncio.as_completed(tasks):
    is_relevant_result = await task
    if is_relevant_result:
        relevant_count += 1
    else:
        irrelevant_count += 1

# Calculate the score and determine success
self.score = relevant_count / (relevant_count + irrelevant_count)
self.success = self.score >= self.threshold

return self.score

# Update evaluate function to use the asynchronous a_measure method
import asyncio

class EvaluationDataset:
    # ...previous methods

    def evaluate(self, metric):
        # Define an asynchronous inner function to handle concurrency
        async def evaluate_async():
            tasks = []  # Initialize the tasks list

            # Schedule a_measure for each test case to run concurrently
            for test_case in self.test_cases:
                task = asyncio.create_task(self.a_measure(test_case, metric))
                tasks.append(task)

            # Wait for all scheduled tasks to complete
            results = await asyncio.gather(*tasks)

            # Process results
            for test_case, result in zip(self.test_cases, results):
                print(test_case, result)

        # Run the asynchronous evaluation
        asyncio.run(evaluate_async())

Users of DeepEval have reported that this decreases evaluation time from hours to minutes. If you’re looking to build a scalable evaluation framework, speed optimization is definitely something that you shouldn’t overlook.

Caching Results and Error Handling

Here’s another common scenario : 

• You evaluate a dataset with 1000 test cases, it fails on the 999th test case, and now you’ve to rerun 999 test cases just to finish evaluating the remaining one. 

Given how costly each metric run can get, you’ll want an automated way to cache test case results so that you can use it when you need to. For example, you can design your LLM evaluation framework to cache successfully test cases and optionally use it whenever you run into the scenario described above. 

Caching is a bit too complicated of an implementation to include in this article, and you’ve personally spent more than a week on this feature when building on DeepEval. Another option is to ignore errors raised. This is much more straightforward since you can wrap each metric execution in a try-catch block. But what good is ignoring errors if you must rerun every test case to execute previously errored metrics? If you want automated, memory-efficient caching for LLM evaluation results, just use DeepEval.

Logging Hyperparameters

The ultimate goal of LLM evaluation is to determine the optimal hyperparameters for your LLM systems. To achieve this, you must associate hyperparameters with evaluation results. This is fairly straightforward, but the difficulty lies in gaining metrics results based on different filters of hyperparameter combinations. 

This is a UI problem, which DeepEval also solves through its integration with Confident AI. Confident AI is an evaluation platform for LLMs, and you can sign up here and try it for free.

Automating Your LLM Evaluation Framework with CI/CD Integration

What good is an LLM evaluation framework if LLM evaluation isn’t automated? (Here is another great read on how to unit test RAG applications in CI/CD.) You’ll need to restructure your LLM evaluation framework to work in a notebook or Python script and in a CI/CD pipeline where unit testing is the norm. 

Fortunately, in the previous implementation for contextual relevancy, we already included a threshold value that can act as a “passing” criterion, which you can include in CI/CD testing frameworks like Pytest.

Related Reading

• How to Fine Tune LLM
• How to Build Your Own LLM
• LLM Function Calling
• LLM Prompting
• What LLM Does Copilot Use
• LLM Evaluation Metrics
• LLM Use Cases
• LLM Sentiment Analysis
• LLM Benchmarks
• Best LLM for Coding

20 LLM Model Evaluation Benchmarks

LLM benchmarks are sets of tests that help assess the capabilities of a given LLM model. They answer questions like: 

• Can this LLM handle coding tasks well?
• Does it give relevant answers in a conversation? 
• How well does it solve reasoning problems? 

You can think of each LLM benchmark as a specialized “exam.” Each benchmark includes a set of text inputs or tasks, usually with correct answers provided, and a scoring system to compare the results.For example, the MMLU (Massive Multitask Language Understanding) benchmark includes multiple-choice questions on:

• Mathematics
• History
• Computer science
• Law and more

After you run an LLM through the benchmark, you can assess the correctness of its answers against the “ground truth” and get a quantitative score to compare and rank different LLMs.While MMLU tests general knowledge, there are benchmarks targeting other areas, such as:

• Language skills: Including logical inference and text comprehension.
• Math problem-solving: With tasks from basic arithmetic to complex calculus.
• Coding: Testing the ability to generate code and solve programming challenges.
• Conversation: Assessing the quality of responses in a dialogue.
• Safety: Check if models avoid harmful responses and resist manipulation.
• Domain-specific knowledge: This includes fields like law and finance.

LLM benchmarks vary in difficulty. Early ones focused on basic tasks like classifying text or completing sentences, which worked well for evaluating smaller models like BERT. Now, with powerful models like:

• GPT
• Claude
• LLaMA

Benchmarks have become more sophisticated and often include complex tasks requiring multi-step reasoning. Research groups, universities, tech companies, and open-source communities create LLM benchmarks. Many benchmarks are shared under open-source or other accessible licenses so developers and researchers can easily use them. 

Why We Need LLM Benchmarks

1. 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.

2. Progress Tracking and Fine-Tuning 

LLM benchmarks also serve as progress markers. You can assess whether new modifications enhance the performance by comparing new LLMs with their predecessors. 

We can already see a history of specific benchmarks becoming 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. A safety benchmark can show how well a given LLM handles novel threats. It guides the fine-tuning process and helps LLM researchers advance the field. 

3. Model Selection 

For practitioners, benchmarks also provide a useful reference 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 solid conversational skills to engage in dialogue, maintain context, and 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 LLM Benchmarks Work

Benchmarks expose models to various test inputs and measure their performance using standardized metrics for easy comparison and ranking. Let’s explore the process step by step! 

1. Dataset Input and Testing 

A benchmark includes tasks for a model to complete, like solving math problems, writing code, answering questions, or translating text. The number of test cases (ranging from dozens to thousands) and how they’re presented will vary by benchmark.

It’s a dataset of text inputs:

The LLM must process each input and produce a specific response, such as completing a sentence, selecting the correct option from multiple choices, or generating free-form text. For coding tasks, the benchmark might include actual coding challenges, like asking to write a specific function. Some benchmarks also provide prompt templates to instruct the LLM on input processing.Most benchmarks come with a set of “ground truth” answers to compare against, though alternative evaluation methods exist, like Chatbot Arena, which uses crowdsourced human labels. The LLM doesn’t “see” these correct answers while completing the tasks; they’re only used later for evaluating response quality.

2. Performance Evaluation and Scoring 

Once the model completes the benchmark tasks, you can measure its quality! Each benchmark includes a scoring mechanism to quantify an LLM's performance, with different evaluation methods suited to different task types. 

Here are some examples:

• Classification Metrics like accuracy: These metrics are ideal for tasks with a single correct answer. The MMLU benchmark uses multiple-choice questions, allowing us to calculate the percentage of correct responses across the dataset simply.
• Overlap-based metrics like BLEU and ROUGE: They are used for tasks like translation or free-form responses, where various phrasing options are valid, and an exact match is rare. These metrics compare common words and sequences between the model’s response and the reference answer.
• Functional code quality: Some coding benchmarks, like HumanEval, use unique metrics such as pass@k, which reflects how many generated code samples pass unit tests for given problems.
• Fine-tuned evaluator models: The TruthfulQA benchmark uses a fine-tuned evaluator called "GPT-Judge" (based on GPT-3) to assess the truthfulness of answers by classifying them as true or false. 
• LLM-as-a-judge: MT-bench introduced LLM-based evaluation to approximate human preferences. This benchmark, featuring challenging multi-turn questions, uses advanced LLMs like GPT-4 as judges to evaluate response quality automatically.

3. LLM Ranking and LLM Leaderboards 

As you run multiple LLMs through the benchmark, you can rank them based on their achieved scores. A leaderboard, a ranking system that shows how different models perform on a specific benchmark or set of benchmarks, is one way to visualize how different models compare.

Many benchmarks come with their leaderboards, often published with the original research paper introducing the benchmark. When first tested on available models, these leaderboards provide a snapshot of model performance.

Public cross-benchmark leaderboards aggregate scores from multiple benchmarks and are regularly updated as new models are released. For example, Hugging Face hosts an open LLM leaderboard that ranks various open-source models based on popular benchmarks (stay tuned. We’ll cover these in the next chapter!).

Common LLM Benchmarks

There are dozens of LLM benchmarks out there, and more are being developed as models evolve. LLM benchmarks vary depending on the task:

• Text classification
• Machine translation
• Question answering
• Reasoning, etc.

We will cover some of the commonly used ones. We provide a short description for each benchmark, links to publicly available datasets and leaderboards, and supporting research.

1. AI2 Reasoning Challenge (ARC) 

Assets: 

• ARC dataset (HuggingFace)
• ARC leaderboard

Research: Think you have Solved Question Answering? Try ARC, the AI2 Reasoning ChallengeThe 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. 

2. HellaSwag

Assets: 

• HellaSwag dataset (GitHub)
• HellaSwag leaderboard

Paper: Can a Machine Really Finish Your Sentence?HellaSwag is a benchmark designed to test commonsense natural language inference. It requires the model to predict the most likely ending of a sentence. Similar to ARC, HellaSwag is structured as a multiple-choice task. The answers include adversarial options and machine-generated wrong answers that seem plausible and require deep reasoning to rule out. 

3. Massive Multitask Language Understanding (MMLU) 

Assets: 

• MMLU dataset
• MMLU leaderboard
  

Paper: Measuring Massive Multitask Language Understanding by Hendrycks et al. (2020)Massive Multitask Language Understanding (MMLU) evaluates LLMs’ general knowledge and problem-solving abilities across 57 subjects, including elementary mathematics, US history, computer science, and law. The dataset contains over 15 thousand multi-choice tasks from high school to expert level. A model’s score for each subject is calculated as the percentage of correct answers, and the final MMLU score is the average of 57 subject scores.An updated MMLU-Pro benchmark (and Dataset) was recently introduced as an enhanced version of the original MMLU benchmark. It incorporates more challenging, reasoning-focused questions and increases the choice set from four to ten options, making the tasks even more complex.

4. SuperGLUE 

Assets: 

• SuperGLUE dataset
• SuperGLUE leaderboard

Paper: A Stickier Benchmark for General-Purpose Language Understanding Systems by WangSuperGLUE stands for Super General Language Understanding Evaluation. It was introduced as an improved and more challenging version of the original GLUE benchmark that was outperformed by LLMs. SuperGLUE measures how well LLMs handle real-world language tasks, such as understanding context, making inferences, and answering questions. Each task has its own evaluation metric. The final score aggregates these metrics into the overall language understanding score.

5. BigBench 

Assets: 

• BIG-bench dataset
• SuperGLUE leaderboard

Paper: Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelsThe Beyond the Imitation Game Benchmark (BIG-bench) is a collaborative benchmark that tests language models' reasoning and extrapolating capabilities. The benchmark consists of over 200 tasks contributed by 450 authors from 132 institutions. Task topics vary from linguistics and math to biology and physics and beyond. The tasks test LLMs beyond pattern matching and explore whether the models can approach human-level reasoning and understanding. 

6. TruthfulQA 

Assets: 

• TruthfulQA dataset
• TruthfulQA leaderboard

Paper: Measuring How Models Mimic Human FalsehoodsThe TruthfulQA benchmark evaluates how well LLMs generate truthful responses to questions. It identifies whether AI models can avoid generating false or misleading information, particularly in areas where human knowledge is prone to misconceptions. The dataset consists of over 800 questions in 38 categories:

• Health
• Law
• Finance
• Politics

The questions include topics where people often hold false beliefs like urban legends, conspiracy theories, pseudoscience, and myths: 

• Do vaccines cause autism?
• Is the Great Wall of China visible from space?

To perform well, models must avoid generating false answers mimicking popular misconceptions.

7. WinoGrande 

Assets: 

• WinoGrande dataset
• WinoGrande leaderboard

Paper: An Adversarial Winograd Schema Challenge at ScaleThe WinoGrande benchmark is based on the Winograd Schema Challenge, a natural language understanding task that requires models to resolve ambiguities in sentences involving pronoun references. 

WinoGrande offers a significantly larger–44000 tasks–and more complex dataset to improve the scale and robustness against the dataset-specific bias. Questions are formulated as fill-in-a-blank tasks with binary options. To complete the challenge, models must choose the correct option. 

8. GSM8K 

Assets: 

• GSM8K dataset
• GSM8K leaderboard

Paper: Training Verifiers to Solve Math Word ProblemsGSM8K is a dataset of 8500 grade school math problems. To reach the final answer, the models must perform a sequence–between 2 and 8 steps–of elementary calculations using basic arithmetic operations like:

• + (Addition)
• − (Subtraction)
• × (Multiplication)
• ÷ (Division)

A top middle school student should be able to solve every problem. However, even the largest models often need help to perform these multi-step mathematical tasks. 

9. MATH 

Assets: 

• MATH dataset
• MATH leaderboard

Paper: Measuring Mathematical Problem Solving With the MATH DatasetThe MATH benchmark evaluates the mathematical reasoning capabilities of LLMs. It is a dataset of 12,500 problems from the leading US mathematics competitions that require advanced skills in areas like algebra, calculus, geometry, and statistics. Most problems in MATH cannot be solved with standard high-school mathematics tools. Instead, they require problem-solving techniques and heuristics.

Coding Benchmarks

10. HumanEval 

Assets: 

• HumanEval dataset
• HumanEval leaderboard

Paper: Evaluating Large Language Models Trained on Code‍HumanEval evaluates LLMs' code-generating abilities. It tests models' capacity to understand programming-related tasks and generate syntactically correct and functionally accurate code according to the provided specifications. 

Each problem in HumanEval comes with unit tests that verify the code's correctness. These test cases run the generated code with various inputs and check whether the outputs match the expected results–just like human programmers test their code! A successful model must pass all test cases to be correct for that specific task.

11. Mostly Basic Programming Problems (MBPP) 

Assets: 

• MBPP dataset
• MBPP leaderboard

Paper: Program Synthesis with Large Language ModelsBasic 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, and basic algorithms. Each problem contains a task description, an example code solution, and test cases to verify the LLM's output.

12. SWE-bench 

Assets: 

• SWE-bench dataset
• SWE-bench leaderboard

Paper: Can Language Models Resolve Real-World GitHub Issues?SWE-bench (Software Engineering Benchmark) evaluates how well LLMs can solve real-world software issues collected from GitHub. The dataset comprises over 2200 GitHub issues and corresponding pull requests across 12 popular Python repositories. 

Given a codebase and an issue, a model must generate a patch that resolves the issue. To complete the task, models must interact with execution environments, process long contexts, and perform complex reasoning–tasks beyond basic code generation problems.

Conversation And Chatbot Benchmarks 

13. Chatbot Arena 

Assets: 

• Chatbot Arena dataset
• Chatbot Arena leaderboard

Paper: Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Chatbot Arena follows a unique approach: it is an open-source platform for evaluating LLMs by directly comparing their conversational abilities in a competitive environment. Chatbots powered by different LLM systems are paired against each other in a virtual “arena” where users can interact with both models simultaneously. 

The chatbots take turns responding to user prompts. After the conversation, the user is asked to rate or vote for the model that gave the best response. The models' identities are hidden and revealed after the user has voted.

14. MT-Bench 

Assets: MT-bench dataset

Paper: Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena by Zheng et al. (2023)MT-bench is designed to test LLMs' ability to sustain multi-turn conversations. It consists of 80 multi-turn questions from 8 categories: 

• Writing
• Roleplay
• Extraction
• Reasoning
• Math
• Coding
• STEM
• Social science

There are two turns: the model is asked an open-ended question (1st turn), then a follow-up question is added (2nd turn). To automate the evaluation process, MT-bench uses LLM-as-a-judge to score the model’s response for each question on a scale from 1 to 10.

Safety Benchmarks

15. AgentHarm 

Assets: AgentHarm dataset

Paper: A Benchmark for Measuring Harmfulness of LLM Agents

The AgentHarm benchmark was introduced to facilitate research on LLM agent misuse. It includes a set of 110 explicitly malicious agent tasks across 11 harm categories, including fraud, cybercrime, and harassment. To perform well, models must refuse harmful agentic requests and maintain their capabilities following an attack to complete a multi-step task.  

16. SafetyBench 

Assets: SafetyBench dataset

Paper: Evaluating the Safety of Large Language ModelsSafetyBench is a benchmark for evaluating the safety of LLMs. It incorporates over 11000 multiple-choice questions across seven categories of safety concerns, including:

• Offensive content
• Bias
• Illegal activities
• Mental health

SafetyBench offers data in Chinese and English, facilitating the evaluation in both languages. 

17. MultiMedQA 

Assets: MultiMedQA datasets

Paper: Large language models encode clinical knowledge‍The MultiMedQA benchmark measures LLMs' ability to provide:

• Accurate
• Reliable
• Contextually appropriate responses 

In the healthcare domain. It combines six existing medical question-answering datasets spanning professional medicine, research, and consumer queries and incorporates a new dataset of medical questions searched online. The benchmark evaluates model answers along multiple axes: 

• Factuality
• Comprehension
• Reasoning
• Possible harm
• Bias

18. FinBen 

Assets: FinBen dataset

Paper: FinBen: A Holistic Financial Benchmark for Large Language ModelsFinBen 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 need help with advanced reasoning and complex tasks like text generation and forecasting. 

19. LegalBench 

Assets: 

• LegalBench datasets

Paper: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language ModelsLegalBench 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, and rhetorical understanding. 

20. Berkeley Function-Calling Leaderboard 

Assets: 

• BFCL dataset
• BFCL leaderboard

Research: Berkeley Function-Calling LeaderboardBerkeley Function Leaderboard (BFCL) evaluates LLMs' function-calling abilities. The dataset comprises 2000 question-answer pairs in multiple languages–including Python, Java, Javascript, and RestAPI–and diverse application domains. It supports various and parallel function calls and function relevance detection. 

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