BEAL is a deep active learning method that uses Bayesian deep learning with dropout to infer the model’s posterior predictive distribution and introduces an expected confidence-based acquisition function to select uncertain samples. Experiments show that BEAL outperforms other active learning methods, requiring fewer labeled samples for efficient training.
A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.
Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
Support Vector Machine (SVM) algorithm with a focus on classification tasks, using a simple 2D dataset for illustration. It explains key concepts like hard and soft margins, support vectors, kernel tricks, and optimization probles.
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.
This article discusses the importance of understanding and memorizing classification metrics in machine learning. The author shares their own experience and strategies for memorizing metrics such as accuracy, precision, recall, F1 score, and ROC AUC.
use of the LLM to perform next-token prediction, and then convert the predicted next token into a classification label.
