Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
A study investigating whether format restrictions like JSON or XML impact the performance of large language models (LLMs) in tasks like reasoning and domain knowledge comprehension.
This article explores various metrics used to evaluate the performance of classification machine learning models, including precision, recall, F1-score, accuracy, and alert rate. It explains how these metrics are calculated and provides insights into their application in real-world scenarios, particularly in fraud detection.
A Github Gist containing a Python script for text classification using the TxTail API
This article discusses the limitations of Large Language Models (LLMs) in classification tasks, focusing on their lack of uncertainty and the need for more accurate performance metrics. New benchmarks and a metric named OMNIACCURACY have been introduced to assess LLMs' capabilities in both scenarios with and without correct labels.
use of the LLM to perform next-token prediction, and then convert the predicted next token into a classification label.
LLMS popularized zero-shot learning, or "prompt engineering" which is drastically easier to use and more effective than labeling data. You can also retrofit "prompt engineering" onto good old fashion ML like text classifiers.
