LLM Benchmarks: Understanding Language Model Performance

Conor Kelly

LLM Benchmarks: Understanding Language Model Performance

Enterprises are rapidly adopting large language models (LLMs) to enhance their next generation of applications. Foundational models like GPT-4, Claude 3, and Gemini Ultra are versatile and non-deterministic, which makes evaluating their performance complex.

LLM benchmarks provide a standardized, rigorous framework for comparing the capabilities of LLMs across core language-related tasks. Understanding these benchmarks—and their criteria for assessing skills such as question answering, logical reasoning, and code generation—is crucial for making informed decisions when selecting and deploying LLMs.

In this blog, we will discuss how LLM benchmarks work, the most common benchmarks used to compare leading models, the importance of evaluation metrics and how these benchmarks help to choose the right LLM for an application.

What Are LLM Benchmarks?

LLM benchmarks are collections of carefully designed tasks, questions, and datasets that test the performance of language models in a standardized process. Why are benchmarks so important? Benchmarks give us metrics to compare different LLMs fairly. They tell us which model objectively does the job better. They also show us the evolution of a single LLM as it learns and improves.

How Do LLM Benchmarks Work?

At their core, LLM benchmarks operate on a fairly straightforward principle: give the model a task, see how it does, and measure the results. However, there are nuances involved in ensuring reliable evaluation.

The Setup

The benchmark's creators carefully select tasks directly related to a chosen aspect of language processing. This may involve generating summaries, continuing a piece of creative writing, or writing code. A high-quality dataset is then compiled. This might be a collection of questions with known answers, real-world chat conversations, or programming challenges.

The key is that this data must be unbiased and accurately represent how language is used. The performance of the LLM is graded by choosing metrics like accuracy, BLEU score, or perplexity (see more below), depending on the type of task. Human experts also get involved to assess nuances like creativity or coherence.

Running the Test

There are a few ways to present a benchmark to an LLM:

  • Zero-shot: The model is given the task with no prior examples or hints. This showcases its raw ability to understand and adapt to new situations.
  • Few-shot: The LLM is given a few examples of how to complete the task before being asked to tackle similar ones. This reveals how well it can learn from a small amount of data.
  • Fine-tuned: In this case, the LLM is specifically trained on data related to the benchmark task with the aim of maximizing its proficiency in that particular domain. If the fine-tuning is effective, it would demonstrate the model’s optimal performance in the task.

Making Sense of the Results

The chosen metrics calculate how well the LLM's output aligns with expected answers or 'gold standards’. This is the LLM's 'report card'. These results, whether consistently within a narrow range or displaying variability, offer insights into the model's reliability.

Distinguishing whether one LLM's superior performance over another stems from mere luck or an actual disparity in skill is crucial. Therefore, upholding strict statistical integrity is paramount. Given this context, it's vital to specify whether a model is deployed in a zero-shot, few-shot, or fine-tuned capacity for a particular task when benchmarking its performance against competitors.

What Metrics Can Be Used for Comparing LLM Performance?

Before we explain the different benchmarks, it’s important to get an understanding of the common metrics used in these benchmarks and what they tell us:

  • Accuracy: A cornerstone of many benchmarks, this is simply the percentage of answers the LLM gets fully correct.
  • BLEU Score: Measures how closely the LLM's generated text aligns with human-written references. Important for tasks like translation and creative writing.
  • Perplexity: How surprised or confused the LLM seems when faced with a task. Lower perplexity means better comprehension.
  • Human Evaluation: Benchmarks are powerful, but sometimes nuanced tasks call for the expert judgement of LLM output in terms of quality, relevance, or coherence.

No single metric tells the whole story. When choosing an LLM or working to maximise LLM performance, it's vital to consider which metrics align with your specific goals.

Types of LLM Benchmarks

Language is complex, which means there needs to be a variety of tests to figure out how truly capable LLMs are. Here is a selection of some of the most common benchmarks used in assessing the performance of LLMs in prevalent applications of AI, how they work and why they’re useful.

LLM Benchmarks for Chatbot Assistance

Chatbots are one of the most popular real-world applications of LLMs. Benchmarks tailored to chatbots focus on two key areas: conversational fluency and goal-oriented success.

  1. ChatBot Arena: A crowdsourced platform where LLMs have randomised conversations rated by human users based on factors like fluency, helpfulness, and consistency. Users have real conversations with two anonymous chatbots, voting on which response is superior. This approach aligns with how LLMs are used in the real world, giving us insights into which models excel in conversation.

  2. MT Bench: A dataset of challenging questions designed for multi-turn conversations. LLMs are graded (often by other, even more powerful LLMs) on the quality and relevance of their answers. The focus here is less about casual chat and more about a chatbot's ability to provide informative responses in potentially complex scenarios.

ChatBot Arena Leaderboard as of March 17th 2024. Source: HuggingFace.
ChatBot Arena Leaderboard as of March 17th 2024. Source: HuggingFace.

LLM Benchmarks for Question Answering and Language Understanding

These benchmarks aim to assess if an LLM truly "gets" the nuances of language, whether it's finding specific information or demonstrating broader comprehension. Here are some key players:

  1. MMLU (Massive Multitask Language Understanding): A wide-ranging benchmark suite designed to push LLMs beyond the basics. It features over 15,000 questions across 57 diverse tasks, spanning STEM subjects, humanities, and other areas of knowledge. MMLU aims for a comprehensive evaluation. Questions go beyond simple factual recall – they require reasoning, problem-solving, and an ability to understand specialised topics.

  2. GLUE & SuperGLUE: GLUE (General Language Understanding Evaluation) was an early but groundbreaking benchmark suite. SuperGLUE emerged as a response to LLMs quickly outperforming the original GLUE tasks. These benchmarks include tasks like:

    • Natural Language Inference: Does one sentence imply another?
    • Sentiment Analysis: Is the attitude in a piece of text positive or negative?
    • Coreference Resolution: Identifying which words in a text refer to the same thing.

LLM Benchmarks for Reasoning

When LLMs generate impressive text or answer questions, it's tempting to attribute this to genuine "understanding". But are they truly reasoning, or just masters of imitation? Benchmarks designed specifically for reasoning skills aim to answer that question.

  1. ARC (AI2 Reasoning Challenge): ARC confronts LLMs with a collection of complex, multi-part science questions (grade-school level). LLMs need to apply scientific knowledge, understand cause-and-effect relationships, and solve problems step-by-step to successfully tackle these challenges.

    Split into an “Easy Set” and a “Challenge Set”, ARC helps us see if a model is going beyond pattern matching and showcasing true logical reasoning capabilities.

  2. HellaSwag: An acronym for “Harder Endings, Longer contexts, and Low-shot Activities for Situations With Adversarial Generations”, this benchmark focuses on commonsense reasoning. To really challenge LLMs, the benchmark includes deceptively realistic wrong answers generated by "Adversarial Filtering," making the task harder for models that over-rely on word probabilities.

    The LLM is presented with a sentence and multiple possible endings. Its task is to choose the most logical and plausible continuation. Picking the right ending requires having an intuitive understanding of how the world generally works – if I drop a glass, it likely breaks, it wouldn’t do something like sprout wings. HellaSwag tests if an LLM possesses this type of general knowledge.

LLM Benchmarks for Coding

Writing code that looks right isn't the same as writing code that works. Coding benchmarks rigorously test whether LLM-generated code accomplishes the task at hand. Here are a few that push LLMs to their limits:

  1. HumanEval: HumanEval moves past simple text comparisons and focuses instead on whether the LLM's generated code actually works as intended. It presents models with carefully crafted programming problems and evaluates whether their solutions pass a series of hidden test cases. HumanEval spotlights those LLMs that can truly problem-solve, not just imitate code they've seen before.
HumanEval Leaderboard as of March 17th 2024. Source: Paperswithcode.
HumanEval Leaderboard as of March 17th 2024. Source: Paperswithcode.
  1. MBPP: Short for “Mostly Basic Python Programming'', MBPP is a vast dataset of 1,000 Python coding problems designed for beginner-level programmers. This benchmark tests an LLM's grasp of core programming concepts and its ability to translate instructions into functional code. MBPP problems comprise three integral components: task descriptions, correct code solutions, and test cases to verify the LLM's output.

  2. SWE-bench: Short for “Software Engineering Benchmark”, SWE-bench is a comprehensive benchmark designed to evaluate LLMs on their ability to tackle real-world software issues sourced from GitHub. This benchmark tests an LLM's proficiency in understanding and resolving software problems by requiring it to generate patches for issues described in the context of actual codebases. Notably, SWE-bench was used to compare the performance of Devin, the AI Software Engineer, with that of assisted foundational LLMs.

Get More Advice on LLM Benchmarks

Humanloop makes it easy to evaluate the performance of LLM applications. We provide enterprises with generative-AI native tooling for testing, measuring and improving applications built on models like GPT-4, Claude 3 and Gemini Ultra.

Using our evaluation features, teams can develop custom benchmarks and perform manual (human expert) or automated (code or LLM-based) evaluation runs to ensure the performance of their models is optimized for production. Book a demo with our team, who have expertise in LLM Evaluations, to learn more.

About the author

Conor Kelly
Conor Kelly is the Growth lead at Humanloop. He has built businesses featured in high-profile media outlets, such as The Late Late Show. Conor is passionate about the potential of AI to transform industries.