The benchmarks for evaluating models provide insights into the capabilities of each model and allow for effective comparison to help users of determine where and how to fine-tune the models and with what additional data to enable practical deployment.


Generative AI models have evolved significantly over the years, making it increasingly difficult to assess and determine which is best. It has become essential to establish reliable evaluation frameworks that can accurately judge the quality of models. 


Moreover, a proper framework will help authorities and concerned agencies to assess the safety, accuracy, reliability, or usability issues of a model.


None of the frameworks are sufficient on their own nor do they consider safety as a factor for evaluation.

Below are some of the important factors that should be present within a framework:


  1. Authenticity
    The accuracy of the results generated by LLMs is crucial. This includes the correctness of facts, as well as the accuracy of inferences and solutions.
  2. Speed
    The speed at which the model can produce results is important, especially when it needs to be deployed for critical use cases. While a slower model may be acceptable in some cases, rapid action teams require quicker models.
  3. Grammar and Readability
    LLMs must generate language in a readable format. Ensuring proper grammar and sentence structure is essential.
  4. Bias
    It’s crucial that LLMs are free from social biases related to gender, race, and other factors.
  5. Backtracking
    Knowing the source of the model’s inferences is necessary for humans to double-check its basis. Without this, the performance of LLMs remains a black box.
  6. Safety & Responsibility
    Guardrails for AI models are necessary. Although companies are trying to make these responses safe, there’s still significant room for improvement.
  7. Understanding the Context
    When humans consult AI chatbots for suggestions about their general and personal life, it’s important that the model provides better solutions based on specific conditions. The same question asked in different contexts may have different answers.
  8. Text Operations
    LLMs should be able to perform basic text operations such as text classification, translation, summarization, and more.
  9. IQ
    Intelligence Quotient is a metric used to judge human intelligence and can also be applied to machines.
  10. EQ
    The emotional Quotient is another aspect of human intelligence that can be applied to LLMs. Models with higher EQ will be safer to use.
  11. Versatility
    The number of domains and languages that the model can cover is another important factor to consider. It can be used to classify the model into General AI or AI specific to a given set of field(s).
  12. Real-time Update
    A system that’s updated with recent information can contribute more broadly and produce better results.
  13. Cost
    The cost of development and operation should also be considered.
  14. Consistency
    Same or similar prompts should generate identical or almost identical responses, else ensuring quality in commercial deployment will be difficult.
  15. Extent of Prompt Engineering
    The level of detailed and structured prompt engineering needed to get the optimal response can also be used to compare two models.

Benchmark Prompting Approaches

Different frameworks utilize various prompting approaches and may provide independent scores for each respective prompt:

  • Zero Shot – The model predicts the answer given only a natural language description of the task
  • One Shot – In addition to the task description, the model sees a single example of the task 
  • Few Shot – In addition to the task description, the model sees a few examples of the task
  • Chain of Thought (COT) – Bears resemblance to few-shot prompting, as it incorporates one or more example completions within the prompt. The difference is that the completions contain detailed thought processes, which are particularly relevant for tasks like solving arithmetic problems.   


Generally speaking, multi-shot approaches yield higher benchmark results than zero shot approaches.

Large Language Model Benchmarking Frameworks

Due to the large number of large language models and the growing number of benchmarking frameworks, EleutherAI developed a “harness” that allows developers to automate the benchmarking of the LLM models using a few-shot approach.


Hugging Face maintains an LLM leaderboard that tracks the benchmark performance of the latest open-source models across numerous frameworks.


Stanford University developed HELM (Holistic Evaluation of Language Models), a comprehensive framework for evaluating foundation models. A leaderboard tracks the performance of the evaluated models. 

Image Generation Benchmarking Frameworks

Video Generation Benchmarking Frameworks

Audio Generation Benchmarking Frameworks




Prompt Engineering Guides



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