LLMs have proven to be highly effective in achieving outstanding results in text classification tasks such as sentiment analysis and labeling. With our recent milestone of 500k classifications using LLMs and fine-tuned models, we have gained valuable insights. One interesting finding is that LLMs have a preference for generating text rather than nothing, which led to false positives. This issue was resolved by adding a catch-all class like “other” or “none-of-these”. Additionally, monitoring hallucinations can provide insights into the quality of class names. Combining fine-tuned classification models with LLMs can enhance cost-efficiency and reduce latency. Lastly, prompt engineering and standardizing input are effective strategies for improving accuracy. We have developed Gloo, an automated tool for solving text-classification problems, which offers various features and capabilities. To experience the benefits of our solution, reach out to [email protected] for a free trial.
https://www.trygloo.com/blog/classify-text-llms-learnings