How to conduct LLM evaluation based on Arthur Bench?

Hello folks, I am Luga. Today we will talk about technologies related to the artificial intelligence (AI) ecological field - LLM evaluation.

1. Challenges faced by traditional text evaluation

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

Although these methods have performed well in the past, with the continuous development of LLM ecological technology, they seem to be slightly inadequate and unable to fully meet current needs.

With the rapid development and continuous improvement of LLM technology, we are facing new challenges and opportunities. LLM continues to improve in capabilities and performance levels, which makes word frequency-based evaluation methods (such as BLEU) potentially unable to fully capture the quality and semantic accuracy of LLM-generated text. LLM can generate more fluent, coherent and semantically rich text, while traditional word frequency-based evaluation methods are difficult to accurately evaluate these advantages.

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 the special characteristics of LLM and its performance on specific tasks. LLMs may exhibit different behavior and performance than pre-trained models when handling specific tasks, so relying solely on evaluation methods based on pre-trained models may not fully assess the capabilities of LLMs.

2. Why is LLM guidance assessment required? And what challenges does it bring?

Generally speaking, in actual business environments, the value of the LLM method is mainly reflected in its "speed" and " Sensitivity", these two aspects are the most important evaluation indicators.

1. Efficient

First, generally speaking, implementation is faster. Compared to the amount of work required by previous assessment pipelines, creating a first implementation of an LLM-guided assessment is relatively quick and easy. For LLM-guided assessment, we only need to prepare two things: describe the assessment criteria in words, and provide some examples for use in the prompt template. Relative to the amount of work and data collection required to build your own pre-trained NLP model (or fine-tune an existing NLP model) to serve as an estimator, using an LLM to accomplish these tasks is more efficient. With LLM, iteration of the evaluation criteria is much faster.

2. Sensitivity

LLM usually exhibits higher sensitivity. This sensitivity may have its positive side, as LLM is more flexible in handling various situations than pre-trained NLP models and the previously discussed evaluation methods. However, this high sensitivity may also make LLM assessment results difficult to predict. Small changes in LLM's input data can have significant effects, which makes it possible to exhibit greater volatility when handling specific tasks. Therefore, when evaluating LLM, special attention needs to be paid to its sensitivity to ensure the stability and reliability of the results.

As we discussed earlier, LLM evaluators are more sensitive than other evaluation methods. There are many different ways to configure LLM as an evaluator, and its behavior can vary greatly depending on the configuration chosen. Meanwhile, another challenge is that LLM evaluators can get stuck if the evaluation involves too many inferential steps or requires processing too many variables simultaneously.

Due to the characteristics of LLM, its evaluation results may be affected by different configurations and parameter settings. This means that when evaluating LLMs, the model needs to be carefully selected and configured to ensure that it behaves as expected. Different configurations may lead to different output results, so the evaluator needs to spend some time and effort to adjust and optimize the settings of the LLM to obtain accurate and reliable evaluation results.

Additionally, evaluators may face some challenges when faced with evaluation tasks that require complex reasoning or the simultaneous processing of multiple variables. This is because the reasoning ability of LLM may be limited when dealing with complex situations. The LLM may require additional effort to address 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 generative text models (LLM). It can be used to evaluate different LLM models, cues, and hyperparameters and provide detailed reports on LLM performance on various tasks.

The main features of Arthur Bench include:The main features of Arthur Bench include:

• Compare different LLM models: Arthur Bench can be used to compare the performance of different LLM models, including models from different vendors, different versions of models, and models using different training data sets.
• Evaluate Tips: Arthur Bench can be used to evaluate the impact of different tips on LLM performance. Prompts are instructions used to guide LLM in generating text.
• Testing 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, and the specific detailed analysis is as follows:

1. Task Definition

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

• Q&A: Testing LLM for open-ended, challenge Ability to understand and answer ambiguous or ambiguous questions.
• Summary: Evaluate LLM's ability to extract key information from text and generate concise summaries.
• Translation: Examine LLM’s ability to translate accurately and fluently between different languages.
• Code generation: Test the ability of LLM to generate code based on natural language descriptions.

2. Model selection

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

3. Parameter configuration

After completing the model selection, proceed to fine-tuning. To more accurately evaluate LLM performance, Arthur Bench allows users to configure hints and hyperparameters.

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

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

4. Assessment run: automated process

The last step is to conduct task assessment with the help of automated process. Typically, Arthur Bench provides an automated assessment process that requires simple configuration to run assessment tasks. It will automatically perform the following steps:

• Call the LLM model and generate text output.
• For specific tasks, apply corresponding evaluation indicators for analysis.
• Generate detailed reports and present evaluation results.

4. Arthur Bench usage scenario analysis

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 and are of great significance to ensure the validity and reliability of the model. In this process, Arthur Bench's role was crucial. His goal is to provide companies with a reliable comparison framework to help them make informed decisions among the many large language model (LLM) options through the use of consistent metrics and evaluation methods.

Arthur Bench will apply his expertise and experience to evaluate each LLM option and ensure consistent metrics are used to compare their benefits and Disadvantages. He will consider factors such as model performance, accuracy, speed, resource requirements and more to ensure companies can make informed and clear choices.

By using consistent metrics and evaluation methodologies, Arthur Bench will provide companies with a reliable comparison framework, allowing them to fully evaluate the benefits and limitations of each LLM option. This will enable companies to make informed decisions to maximize the rapid advances in artificial intelligence and ensure the best possible experience with their applications.

2. Budget and Privacy Optimization

When choosing an artificial intelligence model, not all applications require the most advanced or expensive large language models (LLM). In some cases, mission requirements can be met using less expensive AI models.

This budget optimization approach can help companies make wise choices with limited resources. Instead of going for the most expensive or state-of-the-art model, choose the right one based on your specific needs. The more affordable models may perform slightly worse than state-of-the-art LLMs in some aspects, but for some simple or standard tasks, Arthur Bench can still provide a solution that meets the needs.

Additionally, Arthur Bench emphasized that bringing the model in-house allows for greater control over 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 gain greater control over the processing and storage of data and better protect data privacy.

3. Translate 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 domain and can effectively evaluate the performance of the model in that task or domain.

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

Arthur Bench helps translate academic benchmarks into real-world performance. It achieves this goal in the following ways:

• Provides a comprehensive set of evaluation indicators covering multiple aspects of the model's accuracy, efficiency, robustness, etc. 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 enables enterprises to choose the model that best suits their application scenarios.
• Provides visual analysis tools to help enterprises intuitively understand the performance differences of different models. This enables businesses to make decisions more easily.

5. Arthur Bench Feature Analysis

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

1. Full set of scores Metrics

Arthur Bench has a comprehensive set of scoring metrics covering everything from summary quality to user experience. He can use these scoring metrics at any time to evaluate and compare different models. The combined use of these scoring metrics can help him fully understand the strengths and weaknesses of each model.

The scope of these scoring indicators is very wide, including but not limited to summary quality, accuracy, fluency, grammatical correctness, context understanding ability, logical coherence, etc. Arthur Bench will evaluate each model against these metrics and combine the results into a comprehensive score to assist companies in making informed decisions.

Additionally, if a company has specific needs or concerns, Arthur Bench can create and add custom scoring metrics based on the company's requirements. This is done to better meet the company's specific needs and ensure that the assessment process is consistent with the company's goals and standards.

2. Local version and cloud-based version

For those who prefer local deployment and autonomous control, you can download the Get access to the GitHub repository and deploy Arthur Bench to your local environment. In this way, everyone can fully master 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 prefer convenience and flexibility, cloud-based SaaS products are also available. You can choose to register to access and use Arthur Bench through the cloud. This method eliminates the need for cumbersome local installation and configuration, and enables you to enjoy the provided functions and services immediately.

3. Completely open source

As an open source project, Arthur Bench shows its typical open source characteristics in terms of transparency, scalability and community collaboration. This open source nature provides users with a wealth of advantages and opportunities to gain a deeper understanding of how the project works, and to customize and extend it to suit their needs. At the same time, the openness of Arthur Bench also encourages users to actively participate in community collaboration, collaborate and develop with other users. This open cooperation model helps promote the continuous development and innovation of the project, while also creating 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. Partnerships with Amazon Web Services and Cohere further advance the framework, encouraging developers to create new metrics for Bench and contribute to advances in the field of language model evaluation.

Reference:

• [1] https://github.com/arthur-ai/bench
• [2] https://neurohive.io/ en/news/arthur-bench-framework-for-evaluating-language-models/

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