Reasoning is not only an essential aspect of human intelligence but also one of the main research topics in artificial intelligence. With the recent revolutionary developments in natural language processing, it has been observed that large language models possess a degree of reasoning capabilities. When elicited by prompting, these models can exhibit impressive performance in various reasoning tasks. In this paper, we survey the recent advances in reasoning by prompting large language models. We provide an overview of key benchmarks and categorize the different reasoning methods. Our survey focuses on the most recent advancements in this field and seeks to provide a comprehensive understanding of the current state-of-the-art.

This paper describes a semi-supervised regularized method for additive logistic regression. The graph regularization term of the combined functions is added to the original cost functional used in AdaBoost. This term constrains the learned function to be smooth on a graph. Then the gradient solution is computed with the advantage that the regularization parameter can be adaptively selected. Finally, the function step-size of each iteration can be computed using Newton-Raphson iteration. Experiments on benchmark data sets show that the algorithm gives better results than existing methods.