12 accepted papers
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-co…
We recently developed a joint speech and language model (SLM [1]) which fuses a pretrained foundational speech model and a large language model (LLM), while preserving the in-context learning capability intrinsic to the pretrained LLM. In this paper, we apply SLM to dialog applications where the dia…
We present a scalable, bottom-up and intrinsically diverse data collection scheme that can be used for high-level reasoning with long and medium horizons and that has 2.2x higher throughput compared to traditional narrow top-down step-by-step collection. We collect realistic data by performing any u…
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasing model sizes coupled with hardware shortages has limited affordable access and poses a pressing need for efficiency ap…
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics…
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where i…
Knowledge (including structured knowledge such as schema and ontology and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains. Traditionally, such domain-specific knowledge is encoded implicitly into model parameters for the…
Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To a…
Unitary Evolution Recurrent Neural Networks (uRNNs) have three attractive properties: (a) the unitary property, (b) the complex-valued nature, and (c) their efficient linear operators [1]. The literature so far does not address - how critical is the unitary property of the model? Furthermore, uRNNs…
In this paper, we continue our investigation into copula models for real-valued multivariate features with the goal of compensating for the mismatch in the training and the testing conditions. Previously, we reported results on UCI classification tasks where our method consistently outperformed othe…
Long Short Term Memory Recurrent Neural Networks (LSTM RNNs), combined with hidden Markov models (HMMs), have recently been show to outperform other acoustic models such as Gaussian mixture models (GMMs) and deep neural networks (DNNs) for large scale speech recognition. We argue that using multi-st…
Univariate densities can be modeled accurately and efficiently using nonparametric kernel density estimators, which unfortunately cannot be easily extended to the multivariate case. As an alternative, Gaussian mixture model is used to approximate underlying multivariate distributions, especially bec…