However, the article also points out that this quick process doesn't include data collection, which is necessary for measuring the classifier's performance. But with the classifier in place, data annotation can be done more efficiently using an active learning approach. The author concludes by emphasizing the significance of this productivity gain in text classifier development, often overshadowed by the hype around chatbots and agents.
Key takeaways:
- Before the advent of Language Learning Models (LLMs), setting up a text classifier from scratch would take approximately 3 weeks or 7,200 minutes.
- With the introduction of LLMs, the time required to set up a text classifier has been reduced to just a minute, resulting in a 7,200x productivity gain.
- While using LLMs, no data is initially collected to measure the performance of the classifier. However, the classifier can be evaluated more efficiently using an active learning approach.
- Despite the hype around chatbots and agents, the significant productivity gain in text classifier development due to LLMs is also noteworthy.