How to conduct LLM evaluation based on Arthur Bench?

Hello folks, this is Luga. Today, let’s talk about the technology-LLM evaluation related to the ecological field of artificial intelligence (AI).

How to conduct LLM evaluation based on Arthur Bench?

1. Challenges faced by traditional text evaluation

In recent years, with the rapid development and improvement of large-scale language models (LLM), traditional text evaluation methods may no longer be applicable in some ways.In the field of text evaluation, we may have heard of some methods, such as evaluation methods based on “word occurrence”, such as
BLEU, and evaluation methods based on “pre-trained natural language processing models”, such as BERTScore.

Although these methods have been excellent in the past, with the continuous development of LLM’s ecological technology, they seem to be a bit powerless and unable to fully meet the current needs.

With the rapid development and improvement of LLM, we are facing new challenges and opportunities.The ability and performance level of LLM continue to improve, which makes evaluation methods based on the occurrence of words (such as
BLEU) may not fully capture the quality and semantic accuracy of the text generated by LLM.LLM
It can generate smoother, coherent, and semantically rich text, but traditional evaluation methods based on word occurrence cannot accurately measure the advantages of these aspects.

In addition, evaluation methods based on pre-trained models (such as BERTScore) also face some challenges.Although pre-trained models perform well on many tasks, they may not fully take into account LLM
The unique characteristics of and its performance in specific tasks.LLM may exhibit different behaviors and performance from the pre-trained model when dealing with specific tasks, so relying only on evaluation methods based on the pre-trained model may not be able to fully evaluate
The ability of LLM.

2. Why do you need LLM guidance and evaluation?And the challenges it brings?

Generally speaking, in actual business scenarios, the most valuable aspects of using LLM to guide the evaluation of this method are mainly “speed” and “sensitivity”.


First of all, generally speaking, the implementation speed is faster.Compared with the workload required for the previous evaluation pipeline, the first implementation of creating an LLM-guided evaluation is relatively fast and easy.For LLM
For the guided evaluation, we only need to prepare two things: describe the evaluation criteria in text, and provide some examples used in the prompt template.Compared to building your own pre-trained NLP model (or fine-tuning existing NLP
Model) to be used as the workload and data collection required for the evaluator, and it is more efficient to use LLM to complete these tasks.Using LLM, the iteration speed of evaluation criteria is much faster.


Secondly, LLM is usually more sensitive.This sensitivity may bring positive aspects. Compared with the pre-trained NLP model and the evaluation methods discussed earlier, LLM
Be able to handle these situations more flexibly.However, this sensitivity may also cause the evaluation results of LLM to become very unpredictable.

As we discussed before, LLM evaluators are more sensitive than other evaluation methods.Will LLM
As an evaluator, there are many different configuration methods, and its behavior may vary greatly depending on the selected configuration.At the same time, another challenge is that if the evaluation involves too many reasoning steps or requires too many variables to be processed at the same time, LLM
Evaluators may be in trouble.

Due to the characteristics of LLM, its evaluation results may be affected by different configurations and parameter settings.This means that for LLM
When evaluating, the model needs to be carefully selected and configured to ensure that its behavior meets expectations.Different configurations may lead to different output results, so the evaluator needs to spend a certain amount of time and energy to adjust and optimize the LLM
To obtain accurate and reliable evaluation results.

In addition, when faced with evaluation tasks that require complex reasoning or multiple variables at the same time, evaluators may face some challenges.This is because LLM’s reasoning ability may be limited when dealing with complex situations.LLM
More effort may be required to solve these tasks to ensure the accuracy and reliability of the assessment.

3. What is Arthur Bench?

Arthur Bench is an open source evaluation tool used to compare the performance of generating text models (LLM).It can be used to evaluate different LLM models, hints, and hyperparameters, and provide information about
Detailed report of LLM’s performance on various tasks.

The main functions of Arthur Bench include: The main functions of Arthur Bench include:

  • Compare different LLM models: Arthur Bench can be used to compare different LLMS
    The performance of the model includes models from different vendors, different versions of the model, and models that use different training data sets.
  • Evaluation tips: Arthur Bench can be used to evaluate the impact of different tips on LLM performance.Prompts are instructions used to guide LLM to generate text.
  • Test hyperparameters: Arthur Bench can be used to test the impact of different hyperparameters on LLM performance.Hyperparameters are settings that control the behavior of LLM.

Generally speaking, the Arthur Bench workflow mainly involves the following stages, the specific detailed analysis is as follows:

How to conduct LLM evaluation based on Arthur Bench?

1. Task definition

At this stage, we need to clarify our evaluation goals. Arthur Bench supports a variety of evaluation tasks, including:

  • Question and answer: Test LLM’s understanding and ability to answer open, challenging, or ambiguous questions.
  • Abstract: Evaluate LLM’s ability to extract key text information and generate concise summaries.
  • Translation: Investigate LLM’s ability to translate accurately and fluently between different languages.
  • Code generation: Test LLM’s ability to generate code based on natural language descriptions.

2. Model selection

At this stage, the main work is to screen the evaluation objects.Arthur Bench supports a variety of LLM models, including from OpenAI, Google AI, and Microsoft
And other leading technologies of well-known institutions, such as GPT-3, LaMDA, Megatron-Turing NLG, etc.We can select specific models for evaluation according to research needs.

3. Parameter configuration

After completing the model selection, the refined regulation work will be carried out next.In order to more accurately evaluate LLM performance, Arthur Bench allows users to configure prompts and hyperparameters.

  • Tip: Guide the direction and content of the text generated by LLM, such as questions, descriptions, or instructions.
  • Hyperparameters: Key settings that control LLM behavior, such as learning rate, training steps, model architecture, etc.

Through refined configuration, we can deeply explore the differences in the performance of LLM under different parameter settings and obtain more valuable evaluation results.

4. Evaluation operation: automated process

The last step is to evaluate the task with the help of an automated process.Under normal circumstances, Arthur Bench
Provide an automated evaluation process, and evaluation tasks can be run with simple configuration.It will automatically perform the following steps:

  • Call the LLM model and generate text output.
  • For specific tasks, the corresponding evaluation indicators are applied for analysis.
  • Generate a detailed report and present the evaluation results.

4. Analysis of Arthur Bench usage scenarios

As the key to a fast, data-driven LLM evaluation, Arthur Bench mainly provides the following solutions, specifically involving:

1.Model selection and verification

Model selection and verification are crucial steps in the field of artificial intelligence, which are of great significance to ensure the effectiveness and reliability of the model.In this process, Arthur Bench
The role is very critical.His goal is to provide companies with a reliable comparison framework that helps them make informed decisions among the many large-scale language model (LLM) options by using consistent indicators and evaluation methods.

How to conduct LLM evaluation based on Arthur Bench?

Arthur Bench will use his expertise and experience to evaluate each LLM
Options, and make sure to use consistent indicators to compare their strengths and weaknesses.He will comprehensively consider factors such as model performance, accuracy, speed, resource requirements, etc. to ensure that the company can make informed and clear choices.

By using consistent indicators and evaluation methods, Arthur Bench will provide companies with a reliable comparison framework that enables them to comprehensively evaluate each LLM
Advantages and limitations of options.This will enable companies to make informed decisions to maximize the rapid development of the field of artificial intelligence and ensure that their applications can get the best experience.

2.Budget and privacy optimization

When choosing an artificial intelligence model, not all applications require the most advanced or expensive large language model (LLM).In some cases, the use of lower-cost artificial intelligence models can also meet task needs.

This method of budget optimization can help companies make informed choices with limited resources.Instead of pursuing the most expensive or advanced model, choose the right model according to specific needs.More affordable models may have slightly lower performance than state-of-the-art models in some aspects
LLM, but for some simple or standard tasks, Arthur Bench can still provide solutions that meet the needs.

In addition, Arthur Bench
It is emphasized that introducing the model internally can better control data privacy.For applications involving sensitive data or privacy issues, companies may prefer to use their own internally trained models rather than relying on external third-party LLMs.By using internal models, companies can better grasp the processing and storage of data and better protect data privacy.

3.Transform academic benchmarks into real-world performance

Academic benchmarks refer to model evaluation indicators and methods established in academic research.These indicators and methods are usually specific to a specific task or field, and can effectively evaluate the performance of the model in that task or field.

However, academic benchmarks do not always directly reflect the performance of the model in the real world.This is because real-world application scenarios are often more complex and need to consider more factors, such as data distribution, model deployment environment, etc.

Arthur Bench can help transform academic benchmarks into real-world performance. It achieves this goal in the following ways:

  • Provide a comprehensive set of evaluation indicators, covering the accuracy, efficiency, robustness and other aspects of the model.These indicators can not only reflect the performance of the model under academic benchmarks, but also the potential performance of the model in the real world.
  • Supports multiple model types and can compare different types of models.This allows companies to choose the model that best suits their application scenarios.
  • Provide visual analysis tools to help companies intuitively understand the performance differences of different models.This makes it easier for companies to make decisions.

5. Characteristic analysis of Arthur Bench

As the key to a fast, data-driven LLM evaluation, Arthur Bench has the following characteristics:

1.Full set of scoring indicators

Arthur Bench
It has a complete set of scoring indicators, covering all aspects from summary quality to user experience.He can use these scoring indicators at any time to evaluate and compare different models.The comprehensive use of these scoring indicators can help him fully understand the advantages and disadvantages of each model.

The scope of these scoring indicators is very wide, including but not limited to summary quality, accuracy, fluency, grammatical correctness, contextual understanding ability, logical coherence, etc.Arthur Bench
Each model will be evaluated based on these indicators, and the results will be integrated into a comprehensive score to assist the company in making informed decisions.

In addition, if the company has specific needs or concerns, Arthur Bench
You can also create and add custom scoring indicators according to the company’s requirements.In this way, it can better meet the specific needs of the company and ensure that the evaluation process is consistent with the company’s goals and standards.

How to conduct LLM evaluation based on Arthur Bench?

2.On-premises and cloud-based versions

For those users who prefer local deployment and autonomous control, you can get access from the GitHub repository and send Arthur Bench
Deploy to your own local environment.In this way, everyone can fully grasp and control the operation of Arthur Bench, and customize and configure it according to their own needs.

On the other hand, for those users who are more inclined to convenience and flexibility, cloud-based SaaS products are also provided.You can choose to register, access and use Arthur through the cloud
Bench.This method does not require cumbersome local installation and configuration, but can immediately enjoy the functions and services provided.

3.Completely open source

Arthur Bench
As an open source project, it shows its typical open source characteristics in terms of transparency, extensibility, and community collaboration.This open source nature provides users with a wealth of advantages and opportunities, allowing them to have a deeper understanding of the working principle of the project, and customize and expand according to their own needs.At the same time, Arthur
The openness also encourages users to actively participate in community collaboration, cooperate and develop with other users.This open cooperation model helps to promote the continuous development and innovation of projects, and at the same time, it also creates greater value and opportunities for users.

In short, Arthur Bench provides an open and flexible framework that enables users to customize evaluation indicators, and has been widely used in the financial field.With Amazon Web
The cooperation between Services and Cohere has further promoted the development of the framework, encouraging developers to create new indicators for Bench and contribute to progress in the field of language model evaluation.

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