Recent advancements in Large Language Models (LLMs) have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particularly through multi-turn malicious coding prompts, remains underexplored. In this work, we introduce code decomposition attacks, where a malicious coding task is broken down into a series of seemingly benign subtasks across multiple conversational turns to evade safety filters. To facilitate systematic evaluation, we introduce \benchmarkname{}, a large-scale benchmark designed to evaluate the robustness of code LLMs against both single-turn and multi-turn malicious prompts. Empirical results across open- and closed-source models reveal persistent vulnerabilities, especially under multi-turn scenarios. Fine-tuning on MOCHA improves rejection rates while preserving coding ability, and importantly, enhances robustness on external adversarial datasets with up to 32.4% increase in rejection rates without any additional supervision.

In this blog, you'll learn what Keras callback is, when it is called, what it can do and how it can be used. When you train a model, you try to achieve the best accuracy on your training data but whereas it's possible to reach really good accuracy on the training set, what we really want is to train models that generalize well to the testing(unseen) data. In trying to achieve this we often come across the concept of overfitting. Since training data is what the network learns from, we take the measure of accuracy on training data as one of our metrics. But the test data may be very different from the training set.

Early stopping is one of the effective and simplest regularization techniques used in training neural networks. Usually, during training, the training loss will decrease gradually, and if everything goes well on the validation side, validation loss will decrease too. When the validation loss hits the local minimum point, it will start to increase again. Which is a signal of overfitting. How can we stop the training just right before the validation loss rise again? Or before the validation accuracy starts decreasing?

In Deep Learning models Keras callbacks functions can play a very significant role. The training of such models can take even days to complete so we should have some function to monitor and control our model. Suppose, if the model is getting overfitted we can stop the training or if we have reached at least loss and for next epoch, it gets increased we can again stop the training. Sometimes due to much complexity in deep learning models, they often get crashed and the training gets stopped. Consider you have already trained it for 3 days and all the training gets wasted.

In Deep Learning models Keras callbacks functions can play a very significant role. The training of such models can take even days to complete so we should have some function to monitor and control our model. Suppose, if the model is getting overfitted we can stop the training or if we have reached at least loss and for next epoch, it gets increased we can again stop the training. Sometimes due to much complexity in deep learning models, they often get crashed and the training gets stopped. Consider you have already trained it for 3 days and all the training gets wasted.

We will be using OpenAI's GPT-2 as the model and Panel as the web dashboard framework. This guide will be split into two parts. In the first part, we will load our model and write a predictions function. In the second, we will build the web application. This tutorial assumes you already have Python 3.7 installed and have some understanding of Language Models.

Building Deep Learning models without callbacks is like driving a car with no functioning brakes -- you have little to no control over the whole process that is very likely to result in a disaster. In this article, you will learn how to monitor and improve your Deep Learning models using Keras callbacks like ModelCheckpoint and EarlyStopping. A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You define and use a callback when you want to automate some tasks after every training/epoch that help you have controls over the training process.