Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from. In this work, we study constrained sampling from such language models: generating text that satisfies user-defined constraints, while maintaining fluency and the model's performance in a downstream task. We propose MuCoLa -- a sampling procedure that combines the log-likelihood of the language model with arbitrary (differentiable) constraints in a single energy function, and then generates samples in a non-autoregressive manner. Specifically, it initializes the entire output sequence with noise and follows a Markov chain defined by Langevin Dynamics using the gradients of the energy function. We evaluate MuCoLa on text generation with soft and hard constraints as well as their combinations obtaining significant improvements over competitive baselines for toxicity avoidance, sentiment control, and keyword-guided generation.

In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigation of this incremental symbol learning scenario. Our analysis reveals a troubling quirk in building broad-coverage NLU systems: as the training dataset grows, performance on the new symbol often decreases if we do not accordingly increase its training data. This suggests that it becomes more difficult to learn new symbols with a larger training dataset. We show that this trend holds for multiple mainstream models on two common NLU tasks: intent recognition and semantic parsing. Rejecting class imbalance as the sole culprit, we reveal that the trend is closely associated with an effect we call source signal dilution, where strong lexical cues for the new symbol become diluted as the training dataset grows. Selectively dropping training examples to prevent dilution often reverses the trend, showing the over-reliance of mainstream neural NLU models on simple lexical cues. Code, models, and data are available at https://aka.ms/nlu-incremental-symbol-learning

While a substantial body of prior work has explored adversarial example generation for natural language understanding tasks, these examples are often unrealistic and diverge from the real-world data distributions. In this work, we introduce a two-stage adversarial example generation framework (NaturalAdversaries), for designing adversaries that are effective at fooling a given classifier and demonstrate natural-looking failure cases that could plausibly occur during in-the-wild deployment of the models. At the first stage a token attribution method is used to summarize a given classifier's behaviour as a function of the key tokens in the input. In the second stage a generative model is conditioned on the key tokens from the first stage. NaturalAdversaries is adaptable to both black-box and white-box adversarial attacks based on the level of access to the model parameters. Our results indicate these adversaries generalize across domains, and offer insights for future research on improving robustness of neural text classification models.

With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fourth in a series on using JumpStart for specific ML tasks. In the first post, we showed how to run image classification use cases on JumpStart.

This post contains a list of the AI-related seminars that are scheduled to take place between 7 November 2022 and 31 December 2022. All events detailed here are free and open for anyone to attend virtually. Does chocolate really cure cancer? Advances and Challenges in Conformal Prediction Speaker: Ryan Tibshirani Organised by: Harvard ML Theory Join the mailing list to find out how to access the seminars. Title to be confirmed Speaker: Tim G. J. Rudner (New York University) Organised by: New York University Please contact the organisers here if you are interested in attending the virtual seminar.

To many consumers, buying life insurance can be painful. Despite insurance companies' substantial investments over the past several years in digitizing customer onboarding and policy binding, progress has been slow and incremental and, for many companies, has fallen short of expectations. Many companies have failed to meaningfully scale their efforts to modernize underwriting. The recent COVID-19 lockdowns and ongoing physical-distancing protocols reinforce the need to rethink underwriting. More than ever, insurance companies must address customer and agent frustration with the still lengthy, high-touch, manual process. With COVID-19, paramedic home visits to conduct medical exams have become highly undesirable--especially for a "pushed" product that is not immediately crucial to the customer. In this environment, risk assessment must shift toward more remote, data-driven models, while distribution must shift from in-person interactions to more online interactions.

Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy function -- a Lyapunov function -- to the case of controlled systems. It is a known fact that most control Lyapunov functions are non-smooth -- so is the case in non-holonomic systems, like wheeled robots and cars. Frameworks for stabilization using non-smooth control Lyapunov functions exist, like Dini aiming and steepest descent. This work generalizes the related results to the stochastic case. As the groundwork, sampled control scheme is chosen in which control actions are computed at discrete moments in time using discrete measurements of the system state. In such a setup, special attention should be paid to the sample-to-sample behavior of the control Lyapunov function. A particular challenge here is a random noise acting on the system. The central result of this work is a theorem that states, roughly, that if there is a, generally non-smooth, control Lyapunov function, the given stochastic dynamical system can be practically stabilized in the sample-and-hold mode meaning that the control actions are held constant within sampling time steps. A particular control method chosen is based on Moreau-Yosida regularization, in other words, inf-convolution of the control Lyapunov function, but the overall framework is extendable to further control schemes. It is assumed that the system noise be bounded almost surely, although the case of unbounded noise is briefly addressed.