Evaluating Large Language Models: Are Modern Benchmarks Sufficient?

With the accelerated development of GenAI, there is a particular focus on its testing and evaluation, resulting in the release of several LLM benchmarks. Each of these benchmarks tests the LLM’s different capabilities–but are they sufficient for a complete real-world performance evaluation?

This blog will discuss some of the most popular LLM benchmarks for evaluating top models like GPT 4o, Gemma 3, or Claude. Further, we will discuss the LLMs’ use in practical scenarios and whether these benchmarks are sufficient for complex implementations like agentic systems.

Evaluation Criteria: Traditional AI vs GenAI

Traditional AI algorithms, such as those used for classification, regression, and time-series forecasting, are typically deterministic systems. This means that for a specific set of inputs, the model is expected to produce a consistent output. While the model’s predictions might deviate from the expected ground truth depending on its training, the output will remain stable when provided with the same input combination.

Standard evaluation metrics, such as accuracy, precision, and root mean square error, quantify the model’s deviation from the ground truth labels to assess its performance. These metrics offer a simple, structured, and objective measure of the AI’s effectiveness.

However, this is not the case for GenAI models. These generative models are non-deterministic, i.e., they produce a sequential output, and each element in the sequence is determined probabilistically. There are no concrete ground truths to compare the model output, making their evaluation tricky.

GenAI models are used in various scenarios, such as general conversation, logical problem-solving, and informative chatbots. Their performance is evaluated based on their ability to process the input and any available context and generate a response relevant to the scenario. Several standardized benchmarks have been established for this purpose. Each of these targets a unique aspect of the model and provides an evaluation score, which is used to judge the model’s performance.

Let’s discuss these benchmarks in detail.

Understanding LLM Benchmarks

Here are some of the most popular benchmarks used for LLM evaluation.

General Knowledge & Language Understanding Benchmarks

Common benchmarks designed to test a model’s natural language understanding include:

1. MMLU Benchmark

The Massive Multi-task Language Understanding (MMLU) benchmark is a general-purpose benchmark designed to evaluate the model against diverse subjects. It contains multiple-choice questions covering 57 subjects, including STEM, social sciences, humanities, and more. The difficulty of the question ranges from elementary to advanced professional.

Here is an example question from the dataset related to business ethics:

_______ such as bitcoin are becoming increasingly mainstream and have a whole host of associated ethical implications, for example, they are______ and more ______. However, they have also been used to engage in _______.

A. Cryptocurrencies, Expensive, Secure, Financial Crime
B. Traditional currency, Cheap, Unsecure, Charitable giving
C. Cryptocurrencies, Cheap, Secure, Financial crime
D. Traditional currency, Expensive, Unsecure, Charitable giving

2. AI2 Reasoning Challenge

The AI2 Reasoning Challenge (ARC) is a collection of 7787 grade-school science questions. The dataset is divided into an easy set and a challenge set, where the challenge set contains questions answered incorrectly by both a retrieval-based algorithm and a word occurrence algorithm.

Here is an example question from the dataset:

Q: George wants to warm his hands quickly by rubbing them. Which skin surface will produce the most heat?

A. Dry palms
B. Wet palms
C. Palms covered with oil
D. Palms covered with lotion

3. SuperGLUE

SuperGLUE is an advanced version of the original General Language Understanding (GLU) benchmark. It consists of 8 language understanding tasks. SuperGLUE includes various tasks like reading comprehension, textual entailment, question answering, and pronoun resolution, making it a more comprehensive benchmark than the original GLUE.

A sample task from the dataset:

Coding Benchmarks

Some benchmarks designed to test a model’s performance against coding-related tasks include:

4. HumanEval

The hand-written evaluation benchmark is a set of programming challenges designed to test a model’s coding capabilities. It was first introduced in “Evaluating Large Language Model Trained on Code” and comprises 164 hand-written programming challenges.

The challenges are hand-written since most LLMs are already trained on data sourced from GitHub repositories. Each problem includes a function signature, docstring, body, and several unit tests, averaging 7.7 tests per problem.

Here is a sample problem from the dataset:

def solution(lst):
    """Given a non-empty list of integers, return the sum of all of the odd elements
    that are in even positions.

    Examples
    solution([5, 8, 7, 1])=12
    solution([3, 3, 3,3, 3]) =9
    solution([30, 13, 24, 321]) =0
    """

LLMs output: return sum(lst[i] for i in range(0,len(lst)) if i % 2 == 0 and lst[i] % 2 == 1)

			

5. CodeXGLUE

The CodeXGLUE benchmark dataset is built to test LLMs’ code understanding and generation. It includes a collection of 10 tasks across 14 datasets and a platform for model evaluation and comparison. The tasks can be divided into 4 higher categories:

1. Code-code: This includes code translation, completion, debugging, and repair.
2. Text-code: This includes code generation from natural language descriptions and analyzing the semantics between code and a text description.
3. Code-text: This includes code summarization and explanation.
4. Text-text: This includes translating code documentation from one natural language to another.

Here is an example from the code translation task:

Code translation task from CodeXGLUE benchmark – Source

6. SWE-Bench

The SWE-Bench benchmark consists of 2294 real-world software engineering problems pulled from GitHub. The tasks involve understanding comments from GitHub pull requests and making relevant changes to the codebase. The LLM is tasked with identifying and solving the issue and running tests to ensure everything runs fine.

Reasoning Benchmarks

Here are some benchmarks that test the model’s ability to conduct logical reasoning to reach a conclusion.

7. GSM8k

GSM8k consists of 8.5k grade-school-level and linguistically diverse mathematics problems. These problems are laid out in natural language, making them challenging for AI models to understand. It tests the LLMs’ ability to break down the natural language problem, form a chain-of-thought, and reach the solution.

Here is an example problem from the dataset:

8. Counterfactual Reasoning Assessment (CRASS)

CRASS provides a novel test scheme utilizing so-called counterfactual conditionals and, more precisely, questionized counterfactual conditionals. A counterfactual is a statement that presents a scenario that might have happened but did not. These are also commonly known as what-if scenarios. The CRASS benchmarks contain multiple such scenarios with alternative realities and test the model’s understanding against these.

A sample scenario from the dataset is:

A woman sees a fire. What would have happened if the woman had fed the fire?

1. The fire would have become larger.
2. The fire would have become smaller.
3. That is not possible.

9. Big-Bench Hard (BBH)

The original Big-Bench benchmark consists of 200 tasks covering domains like arithmetic and logical reasoning, commonsense knowledge, and coding. However, most modern LLMs outperform human raters for many of these tasks. The Big-Bench Hard is a subset of the original, containing 23 challenging tasks for which no LLM outperformed human raters. These tasks challenge the LLMs’ reasoning capabilities and the development of a chain-of-thought.

A sample task from the benchmark is:

Are LLM Benchmarks Sufficient?

LLM benchmarks are a great way to evaluate these models’ performance in real-world scenarios, but the real question remains: Are they sufficient for a generic evaluation? The benchmarks we have discussed above are just a small subset, and there are several more frameworks for numerous other tasks.

Moreover, no single LLM excels in each evaluation since each model is trained for a different purpose. For example, the recently released GPT-4.5 surpasses the older o3-mini in basic language understanding but loses in complex reasoning tasks as it is not a Chain-of-Thought (CoT) reasoning model.

Evaluation scores for GPT-4.5 compared with GPT-4o and o3-mini – Source

So, while each benchmark quantifies the LLM’s performance for a few particular scenarios, these numbers do not portray an overall picture. A single LLM may perform differently on different benchmarks, even within a single domain, since each has slightly different tasks.

It proves that most benchmarks are designed for a specific and rather lenient evaluation. A great example is the Humanity’s Last Exam (HLE) benchmark, which is one of the rare evaluation frameworks designed to be a single unit of measure of the model’s performance. It consists of 2700 extremely challenging and multi-modal tasks across several academic domains. The results for HLE against state-of-the-art models prove how much current LLMs are still lacking and how other benchmarks are insufficient for modern-day evaluation.

Benchmarks demonstrating LLMs poor performance on the HLE benchmark – Source

Another important factor to consider here is that modern systems are now moving towards agentic implementations. Conventional benchmarks may evaluate the model’s generative response but do not assess its performance with an automated agentic system.

Agentic Evaluation: Going Beyond LLMs

An agentic system goes beyond language understanding and data generation. It involves reading real-time data streams, interacting with the environment, and breaking down tasks to complete a set objective. AI agents are gaining popularity, and several interesting and practical use cases have been found in industries like customer support, e-commerce, and finance. They have also been deployed in some unusual but fun situations, such as Anthropics Claude Sonnet 3.7 Sonnet playing Pokemon Red on the original Game Boy.

Conventional benchmark scores do not represent performance in real-world actionable scenarios. These practical systems require specialized benchmarks like AgentBench and t-bench to better judge the agent’s capabilities. These benchmarks test the LLMs’ interaction with modules like databases and knowledge graphs and further evaluate them on multiple platforms and operating systems. Moreover, agentic systems also need to be judged on the time it takes them to complete a certain task compared to humans. Studies suggest that while the task completion time horizon is growing exponentially, agentic systems are presently behind human workers and may take some time to fully automate everyday tasks.

Final Thoughts

The age of GenAI is here, and it’s here to stay. Generative models are being integrated into everyday workflows, automating mundane tasks and improving work efficiency. However, as this adoption increases, it is vital to evaluate these LLM-based systems and how they fare against challenging real-world scenarios.

Several benchmarks have been created for LLM evaluation, and each tests the model in different scenarios. Some evaluate the model’s performance on logical reasoning, while others judge it on its ability to solve programming problems and generate code. However, as newer and smarter models are released, even the most popular benchmarks are proving insufficient in providing a complete evaluation. Benchmarks like the HLE prove that even state-of-the-art models can be found lacking in challenging scenarios.

Moreover, as agentic AI gains traction, we need newer, more robust ways to evaluate end-to-end systems. Conventional benchmarks do not evaluate a model’s understanding of its surroundings or its ability to complete a set objective.

As GenAI progresses, evaluation metrics must evolve to meet the demanding practical requirements. Newer standards must be set to ensure the safe and smooth adoption of AI.

Learn More

• Arize eval templates
• LLM Evaluation Guide
• Agent Evaluation Guide