Large language models (LLMs) have made significant progress in code generation tasks, but their performance in tackling programming problems with complex data structures and algorithms remains suboptimal. To address this issue, we propose an in-context learning approach that guides LLMs to debug by using a "print debugging" method, which involves inserting print statements to trace and analysing logs for fixing the bug. We collect a Leetcode problem dataset and evaluate our method using the Leetcode online judging system. Experiments with GPT-4 demonstrate the effectiveness of our approach, outperforming rubber duck debugging in easy and medium-level Leetcode problems by 1.5% and 17.9%.

It's unsuto hear cats mentioned in a presentation about machine learning. But they actually have more in common than you would think. Ismail Elouafiq, a Data Scientist at SVT has drawn a genius association between machine learning systems. Nevertheless, Ismail's talk at Nordic Data Science and Machine Learning Summit is a great overview of a common problem that occurs in machine learning – machine learning antipatterns. "Imagine you are working in a cat hospital", starts Ismail, and you admitted 132 cats which are victims of jumping off the window.

Increasing use of machine learning techniques in scientific research is leading to a'science crisis', claims Rice University statistician Dr Genevera Allen, who has found that the results produced by...

In late 2017, I started to develop interest in the Machine Learning field. I talked about my experience when I started my journey. In summary, it has been filled with fun challenges and lots of learning. I am an Android Engineer, and this is my experience working on ML projects with our data scientist. I remember attempting to solve an image classification problem that came up in one of our apps.