Chatbot Arena is a platform designed to evaluate and compare large language models (LLMs) in a real-world environment. Its main goal is to provide a transparent and comprehensive assessment of how these models perform across different tasks and contexts. Below are some key aspects of Chatbot Arena and its comparison tool, Side by Side.
Online evaluation of models
- Chatbot Arena focuses on the evaluation of LLMs in real-world conditions, meaning it allows testing the models in a wide variety of contexts and comparing them.
Comparative Benchmarking
- The platform provides tools to compare the performance of different LLMs, allowing users to see how the models behave in direct comparison.
Transparency and Thoroughness
- Chatbot Arena promotes transparency in evaluations by providing detailed metrics and results that help understand the strengths and weaknesses of each model.
Now, one of the most interesting sections: Side-by-Side.
One of the standout features of LMSYS Chatbot Arena is its side-by-side comparison tool, which allows users to compare two models simultaneously on the same screen, responding to the same question or prompt. This is fantastic because, in just a few seconds, we can test various prompts that we know are crucial for us, evaluating how concise, precise, and accurate each model's response is. I highly recommend giving it a try because the results can be surprising—sometimes in favor of, and sometimes against, what we thought was our favorite model... ;)
Let's try side by side:
Model Selection.
- When we are on the page, we will see "Choose two models to compare." We select one model from the dropdown, which will be Model A, and next to it, in the same dropdown, we select the second model, which will be Model B. Once both models are selected from the list of available LLMs, we proceed to write the prompt at the bottom, and we will see how our question (which is for both) appears in each model's window, and both models generate a response based on our text.
Visualization of Responses:
- The responses from both models are displayed side by side on the screen, allowing for a clear and direct comparison of how each model answers the question.
Comparative Evaluation:
- What do we get from these tests? Well, we can evaluate the quality, coherence, and accuracy of each model's responses, helping to identify which one performs better in different scenarios. It is important to note that there is still no one-size-fits-all LLM model that is 100% effective across all fields. For example, an LLM trained primarily for mathematical calculations will not respond in the same way as one trained for image editing or generation, conversation, or video transcription. While it will still provide an answer, the model trained for the specific task we are looking for will likely respond with higher quality and accuracy.
The idea is to compare models that we already know or have information about, which are suitable for our needs, and after testing them, decide which one best suits our way of working.
It is also perfect for testing new models or new versions of LLMs we have already used and want to see how they have improved based on our requirements.
Objective Comparison:
- The side-by-side comparison feature allows for an objective evaluation of each model's performance by providing a clear, visual representation of how each model handles the same prompt. This side-by-side view removes any biases and allows users to directly observe which model delivers more accurate, relevant, and coherent responses, making it easier to choose the right tool for specific tasks.
By comparing models in real-time, users can also assess various aspects such as creativity, tone, verbosity, and precision, ensuring that the chosen model aligns with their needs and preferences.
Model Improvement:
- Developers can use the results from Chatbot Arena to identify areas of improvement in their models and make adjustments based on comparative performance. This valuable feedback allows for fine-tuning the models to enhance their accuracy, coherence, and overall effectiveness in real-world scenarios. By analyzing how different models perform across various tasks, developers can target specific weaknesses and make informed decisions on how to improve their models, ensuring they are better equipped to meet user needs and expectations.
Selection:
- Users and organizations can make informed decisions about which language model to use in their applications based on the comparative results provided by the platform.
Chatbot Arena, with its side-by-side comparison tool, offers a valuable platform for evaluating and benchmarking large language models in the real world by allowing direct comparison of responses generated by different models.
Link to the page to compare models on Chatbot Arena, once on the page, there are tabs at the top; go to Arena Side by Side.
Right now, I’m testing between Claude 3.5 and a new open-source version released by Google, Gemma 2. So far, in tests and math logic prompts, Claude 3.5 is winning.
