High-Tech Software House



LLM Performance Benchmarks

card image

LLM Performance Benchmarks

  • Challenges in choosing the right LLM
  • Examples of LLM benchmarks
  • Aggregating benchmarks
  • Benchmark updating for evolving real-world scenarios
  • Choosing the right LLM for your business

Challenges in choosing the right LLM

The number of Large Language Models (LLMs) skyrockets. Picking the right one is tough given the multitude of the available options.

The hardest, yet the most essential part, is to match the model to specific needs. Factors such as model size, accuracy, inference speed, computational resources, pricing, privacy, and support for various languages and tasks all play crucial roles in this decision-making process. 

Keeping up with new LLM developments and how they affect projects makes choosing even trickier. 

One way to make sure the LLM implementation is done properly is to book a consulting call with Flyps and specify your requirements so that our experts can choose and implement a right model suited for your needs. 

You can book a call here.

As per the general use of widely available pre fine-tuned models, you may have a look at the benchmarks that can be used to evaluate an LLM. 

Examples of LLM benchmarks

The number of benchmarks available for general evaluation of LLMs is growing, with multiple benchmarks assessing their skills in coding, language understanding, and more. Despite debates about their reliability, ranking LLMs remain vital for understanding specific LLMs' strengths and weaknesses.

Several benchmarks frequently referenced when assessing LLMs are highlighted as follows:

  • BIG (Beyond the Imitation Game) -Bench Hard: Introduced by Clark et al. in 2021, this extensive benchmark has over 200 tasks across 10 categories, such as code completion, translation, and creative writing. 
  • MBPP: Features 1,000 beginner-level Python programming problems. It measures the code generation abilities of LLMs. Problems include a task description, solution, and test cases. 
  • MMLU (5-shot): Evaluates LLMs on 57 diverse language tasks in multiple languages, including question answering and translation. Performance is gauged by accuracy and fluency. 
  • TriviaQA (1-shot): Tests LLMs' ability to answer questions using only one training example. It contains 100,000 questions and answers of varied difficulty. 
  • HumanEval: Consists of 164 programming challenges. LLMs must generate Python code based on a given description (docstring). Humans assess the code accuracy. This benchmark encompasses only code prompt assessments. 

You can visit the websites linked in the footnotes corresponding to the mentioned benchmarks to view up-to-date leaderboards.

Aggregating benchmarks

It is worth mentioning the aggregation of multiple benchmarks. Hugging Face, as a leading hub for machine learning models, has initiated a leaderboard for open models. The leaderboard adopts an aggregating benchmarks ranking methodology. 

This approach compiles results from multiple benchmarks to provide a holistic view of a model's performance across diverse tasks and domains. Instead of isolating each benchmark's outcome, the aggregated scores present a comprehensive metric that captures a model's overall proficiency. 

Models are assessed on four main benchmarks (AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA) using the Eleuther AI Language Model Evaluation Harness, a tool designed for comprehensive evaluation of language models. The benchmarks include grade-school science questions, commonsense inference, where humans excel (~95%) but LLMs experience problems, evaluation of multitasking accuracy across 57 subjects like math, history, and law, and measuring a model's tendency to echo common online falsehoods.

Please note that this leaderboard includes chatbots iterations built on the basis of the models with freely available weights and excludes proprietary software. This means that some of the popular models like GPT-4 are not rated there. 

Benchmark updating for evolving real-world scenarios 

The above conventional benchmarks exhibit significant limitations. They are quickly assimilated into training data of the LLMs and one may argue that it becomes difficult to relate them to practical, real-world scenarios, and real-world applications. To address this issue one could potentially turn to the LLMonitor benchmark project.

Firstly, a dynamic dataset that changes weekly presents an evolving perspective for evaluating model performance. This ensures that the LLMs that plausibly would optimize for a static set from the popular benchmarks are not favored in the leaderboards but are periodically tested against fresh and potentially unseen information. Such an approach mirrors real-world scenarios where data and context are constantly changing, providing a more robust and realistic assessment of an LLM's adaptability and generalization capabilities.

Secondly, utilizing crowdsourced and chosen by community vote real-world prompts allows for a broader range of viewpoints and real-life situations in evaluating Large Language Models (LLMs). This method ensures that the testing is comprehensive and mirrors genuine human experiences. Additionally, not only are the prompts coming from a wider group, but the evaluation criteria are also crowdsourced, enhancing the authenticity of the LLM assessment process.

The dataset comprises prompts categorized into five domains: knowledge, code, instruct, creativity, and reflection, providing a multifaceted approach to assess diverse cognitive and computational capabilities. Although the dynamic data set and crowdsourcing support reliability of the benchmarking results, it is important to note that this benchmark uses GPT-4 for grading automation which may introduce a slight bias towards this model.

Choosing the right LLM for your business

Large Language Models have explosive potential in areas like customer operations, marketing, sales, and research, potentially adding trillions to the global economy. The growing number of LLMs poses a challenge in selecting the right model, necessitating benchmarks for evaluating their performance, although these benchmarks have limitations.

Navigating the rapidly evolving landscape of Large Language Models can be challenging, especially when aiming to their full potential for specific business needs. With a deep-rooted expertise in AI solutions, Flyps not only assists in selecting the most suitable LLM tailored to your organizational goals but also ensures its seamless implementation.

Contact us here with your business requirements to be sure that your software is powered by the best LLM suited for your needs.


BIG (Beyond the Imitation Game) -Bench Hard





Hugging Face Benchmark



See our tech insights

card image

LLM, Robotics, biotech

Case study: Enviromental Protection Industry

card image


Using large language models the LlamaIndex (former GPTIndex) with ChatGPT

card image


LLM Performance Benchmarks

card image

Medtech, computer vision

AI Powered Endoscopic Capsule

Demanding project?

We can help you overcome the challenges
Contact us

You can count on us!

Like our satisfied customers from many industries