Run Dual Chat Evaluations with Experimental Dataset: Diagnostic Test

Run Dual Chat Evaluations with Experimental Dataset: Diagnostic Test

Table of Contents

1. Introduction
2. Running the Dual Chat Evaluations
3. The Overall Pipeline and Setup
4. Authenticating and Accessing GCP
5. Configuring the Evaluation
6. Running the Chat Evaluation
7. Viewing the Results
8. Analyzing the Metrics
9. Investigating Improvements in JDK
10. Conclusion

Introduction

In this article, we will explore the process of running Dual Chat evaluations using an experimental dataset called the Diagnostic dataset. This dataset is a subset of data derived from code generations and issues from the Pick dataset. The purpose of this evaluation is to determine the quality of the responses generated by the Dual Chat model by comparing them to the ground truth responses from the foundational model, CLA2. By analyzing the generated embeddings, we can assess the semantic similarity between the responses of the two models. The goal of this evaluation is to allow developers to perform rapid, low-cost experiments and gain confidence in the changes they make to the JDK.

Running the Dual Chat Evaluations

To run the Dual Chat evaluations, we need to follow a specific pipeline. We use Apache Beam to structure our pipelines, which can be executed locally or via Dataflow for larger datasets. The evaluation process starts by reading the test cases from the Diagnostic table in BigQuery. These test cases are then batched into smaller sets and sent to different models, including Dual Chat, which is treated as a Blackbox model in this evaluation. The responses from the models and the Dual Chat API are merged, and then another model is called to perform the evaluations. The evaluations are then written to BigQuery for analysis.

The Overall Pipeline and Setup

The overall pipeline for the Dual Chat evaluations involves several steps. First, the test cases are read from the Diagnostic table in BigQuery. Next, the responses from the models and the Dual Chat API are merged. The evaluations are then written to BigQuery for analysis. The pipeline can be run locally or via Dataflow for larger datasets. The setup for running the evaluations involves authenticating and accessing GCP, configuring the evaluation parameters, and running the evaluation itself.

Authenticating and Accessing GCP

To authenticate and access GCP, you need to set up the necessary credentials. This includes configuring access to read and write from BigQuery and access the Vertex AI API. The instructions for setting up the credentials are provided in the documentation. It is important to carefully follow these instructions to ensure proper authentication. If you encounter any issues, you can reach out to the team for assistance.

Configuring the Evaluation

The evaluation is configured through a config file, which specifies the input and output parameters for the evaluation pipeline. The config file includes options for selecting the answering and comparison models, setting the base URL for the Dual Chat model, and specifying the output location for the evaluation results. Additionally, there are configuration options for setting up a smaller sample size for quicker test runs. It is important to review and modify the config file according to your specific requirements before running the evaluation.

Running the Chat Evaluation

Once the authentication, access, and configuration are set up, the Dual Chat evaluation can be run. The evaluation process involves calling the Dual Chat and Claw 2 models to generate responses to the test cases. The responses are then compared using similarity scores to determine the quality of the Dual Chat model's responses. The evaluation results are stored in BigQuery for further analysis. During the evaluation, it is important to ensure that JDK and AI Gateway are running and powered up to support the evaluation process.

Viewing the Results

After the evaluation is completed, the results can be viewed using different methods. The most common method is to view the results in BigQuery using SQL queries. The results include information such as the answering model, comparison model, and similarity scores between the responses. Another method is to view the results locally by exporting them to a CSV file and using tools like xsv or Microsoft Excel to analyze the data. Viewing the results allows developers to identify areas where the Dual Chat model may be performing poorly and investigate potential improvements.

Analyzing the Metrics

The evaluation metrics provide valuable insights into the performance of the Dual Chat model. Metrics such as correctness, readability, and understanding can be used to assess the quality of the responses. By analyzing these metrics, developers can identify areas where the model may need improvement and make changes to the JDK accordingly. It is important to closely analyze the metrics and prioritize areas for improvement to enhance the performance of the Dual Chat model.

Investigating Improvements in JDK

If the evaluation results indicate areas where the Dual Chat model is not performing well, it is crucial to investigate potential improvements in the JDK. One approach is to modify the JDK and run the evaluation again to see if the changes have a positive impact on the model's responses. By making iterative changes to the JDK, developers can refine the responses of the Dual Chat model and improve its overall performance.

Conclusion

In this article, we have explored the process of running Dual Chat evaluations using an experimental dataset. We have discussed the overall pipeline, authentication and access to GCP, configuring the evaluation, running the evaluation, viewing the results, analyzing the metrics, and investigating improvements in the JDK. By following this process and analyzing the results, developers can gain insights into the performance of the Dual Chat model and make informed decisions to enhance its quality.

Highlights

• Running Dual Chat evaluations using an experimental dataset.
• Comparing responses generated by Dual Chat with those from the foundational model.
• Analyzing semantic similarity between responses using embeddings.
• Rapid and low-cost experiments for JDK changes.
• Viewing and analyzing evaluation results to identify areas for improvement.

FAQ

Q: How many test cases are included in the Diagnostic dataset? A: The Diagnostic dataset includes 20 test cases focusing on code generations.

Q: Can I run smaller sample sizes for quicker test runs? A: Yes, you can specify a smaller sample size in the config file to speed up the evaluation process.

Q: Is it possible to view the evaluation results locally? A: Yes, you can export the results to a CSV file and use tools like xsv or Microsoft Excel to view them locally.

Q: How can I investigate improvements in the JDK? A: By modifying the JDK and running the evaluation again, you can assess the impact of the changes on the Dual Chat model's responses.

Q: What metrics can I analyze to assess the quality of the responses? A: Metrics such as correctness, readability, and understanding can be analyzed to assess the quality of the Dual Chat model's responses.