The Importance of Continuous LLM Training and Maintenance

Yesterday's very extensive report on How Is ChatGPT’s Behavior Changing over Time? ( https://arxiv.org/pdf/2307.09009.pdf ) by Lingjiao Chen, Matei Zaharia , James Zou of 美国斯坦福大学 and 美国加州大学伯克利分校 is making rounds on all the platforms. After studying the report, thought of summarizing my understanding from the document.

As shown by the analysis in this report, the performance of LLMs can shift substantially within a short timespan of just 3-4 months. This highlights the need for continuous training and rigorous monitoring of LLMs after they are deployed.?

The analysis evaluated GPT-3.5 and GPT-4 in March 2023 and then again in June 2023 on diverse tasks like math problem solving, visual reasoning, code generation and answering sensitive questions. Surprisingly, there were major performance fluctuations:

1. GPT-4's accuracy on classifying prime numbers dropped from 97.6% to just 2.4% from March to June.?
2. The percentage of GPT-4's code generations that were executable declined from 52% to 10% over the period.
3. GPT-4 became more reluctant to answer dangerous questions directly, but also gave less explanatory rationale.

Several factors likely contribute to such drifts, including changes to the model architecture, training data, and safety constraints. The key insight is that LLMs are not static - their behavior keeps evolving after initial deployment.

This has two important implications:

• First, LLM users cannot treat services like GPT-3.5 as a "fixed" black box. They need to continuously monitor the LLM's outputs and performance on representative tasks, even if the provider claims it is the "same" model. Failing to do so risks suddenly losing expected functionality.
• Second, LLM providers must continue model training and maintenance to retain quality over time. As data and use cases expand, the models require regular fine-tuning to adapt. Safety constraints also need periodic updating as new vulnerabilities or biases emerge.

In summary, LLMs are not "set it and forget it" systems. To leverage them responsibly over the long term, maintaining rigorous LLM training and monitoring practices is essential. The fluid nature of LLMs necessitates ongoing vigilance and care from both users and providers.