Large language models (LLMs) have demonstrated remarkable capabilities across numerous domains, as highlighted by OpenAI (2023) and Bubeck et al. (2023). However, whereas LLMs pre-trained on extensive language data excel in general language understanding, they may not be optimized for every specific task of interest prompted by instructions. Therefore, there is need for continual instruction learning to adapt LLMs to evolving tasks and domains. Indeed, continual instruction learning is essential for LLMs such as GPT (Radford et al., 2019) to maintain their effectiveness and relevance in handling a wide range of tasks and domains. Such models are trained on vast amounts of text data and fine-tuned for specific applications, often by learning tasks sequentially (Luo et al., 2023), i.e. learning on datasets pertaining to one task all at once, before moving on to the next task. The challenge lies in their ability to continually learn and adapt as they encounter new tasks and information.

Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still a concern. Recent works have shown great improvements in the instruction-following capability via additional training for instruction-following tasks. However, the mechanisms responsible for effective instruction-following capabilities remain inadequately understood. Here, we introduce a simplified instruction-following task and use synthetic datasets to analyze a Transformer-based causal language model. Our findings suggest that the model learns task-specific information by clustering data within its hidden space, with this clustering process evolving dynamically during learning. We also demonstrate how this phenomenon assists the model in handling unseen instances, and validate our results in a more realistic setting. Furthermore, we present inspired applications regarding pre-training and alignment.

Multilayer switch networks are proposed as artificial generators of high-dimensional discrete data (e.g., binary vectors, categorical data, natural language, network log files, and discrete-valued time series). Unlike deconvolution networks which generate continuous-valued data and which consist of upsampling filters and reverse pooling layers, multilayer switch networks are composed of adaptive switches which model conditional distributions of discrete random variables. An interpretable, statistical framework is introduced for training these nonlinear networks based on a maximum-likelihood objective function. To learn network parameters, stochastic gradient descent is applied to the objective. This direct optimization is stable until convergence, and does not involve back-propagation over separate encoder and decoder networks, or adversarial training of dueling networks. While training remains tractable for moderately sized networks, Markov-chain Monte Carlo (MCMC) approximations of gradients are derived for deep networks which contain latent variables. The statistical framework is evaluated on synthetic data, high-dimensional binary data of handwritten digits, and web-crawled natural language data. Aspects of the model's framework such as interpretability, computational complexity, and generalization ability are discussed.