(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs

The academic paper titled “Why Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs” by Wanqin Ma, Chenyang Yang, and Christian Kästner, dated November 18, 2023, addresses the challenges of regression in large language models (LLMs) as they are updated over time.

Novelty:

The paper presents a novel approach to addressing the challenges posed by the evolving nature of LLM APIs. It introduces the concept of regression testing tailored for LLMs, highlighting the dynamic nature of these models and the need for continual adaptation of prompts and testing methods.

Merit:

1. Case Study on Toxicity Detection: The authors conducted an exploratory case study using OpenAI’s GPT-3.5 model family, focusing on the task of toxicity detection. This study is significant as it empirically demonstrates how LLM updates can lead to performance regression, even when overall accuracy improves.
2. Prompting Strategies and Model Updates: The paper identifies that different prompting strategies react differently to model updates, emphasizing the complexity and non-determinism in LLM behavior. This is a crucial finding for developers relying on LLMs for application development.
3. Regression Testing Framework: The proposal for a regression testing framework that accounts for the unique characteristics of LLMs, such as non-determinism and prompt sensitivity, is an innovative solution to a real-world problem.

Commercial Applications:

1. Software Development and Maintenance: The insights and methodologies proposed can significantly aid software developers in maintaining and updating applications that rely on LLM APIs. This includes prompt optimization and versioning.
2. Quality Assurance for AI Products: The regression testing framework can be integrated into the development cycle of AI products, ensuring that updates do not compromise the performance or reliability of the product.
3. Consulting and Training Services: Companies specializing in AI and ML can use the findings to provide consulting services to businesses that are integrating LLMs into their operations. Additionally, this research can be used for training developers in advanced AI application development techniques.

Summary of Findings and Conclusions:

• The study found that 58.8% of prompt-model combinations experienced a drop in accuracy following an API update. Notably, different prompts reacted differently to the same update, complicating the prompt engineering process.
• The authors propose a new approach to regression testing for LLMs, advocating for tests defined over data slices rather than individual predictions, and tracking both model and prompt updates to manage non-determinism.
• The paper concludes that regression in LLMs is a significant issue that requires a fundamental shift in testing strategies. The proposed changes to regression testing practices aim to help developers effectively manage the evolving nature of LLM APIs.

This paper is significant for the field of AI and ML, particularly for application developers and researchers focusing on the operationalization of LLMs. Its pragmatic approach to a real-world problem, backed by empirical research, adds substantial value to the discourse on managing and leveraging evolving AI technologies.