Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matrices from Pre-trained Language Models (PLMs). We investigate multiple tasks for fine-tuning and show that the dialogue-tailored Sentence Ordering task performs best. To locate and exploit discourse information in PLMs, we propose an unsupervised and a semi-supervised method. Our proposals achieve encouraging results on the STAC corpus, with F1 scores of 57.2 and 59.3 for unsupervised and semi-supervised methods, respectively. When restricted to projective trees, our scores improved to 63.3 and 68.1.

Large language models (LLMs) that have been trained on multilingual but not parallel text exhibit a remarkable ability to translate between languages. We probe this ability in an in-depth study of the pathways language model (PaLM), which has demonstrated the strongest machine translation (MT) performance among similarly-trained LLMs to date. We investigate various strategies for choosing translation examples for few-shot prompting, concluding that example quality is the most important factor. Using optimized prompts, we revisit previous assessments of PaLM's MT capabilities with more recent test sets, modern MT metrics, and human evaluation, and find that its performance, while impressive, still lags that of state-of-the-art supervised systems. We conclude by providing an analysis of PaLM's MT output which reveals some interesting properties and prospects for future work.

Empirical studies suggest a deep intertwining between opinion formation and decision-making processes, but these have been treated as separate problems in the study of dynamical models for social networks. In this paper, we bridge the gap in the literature by proposing a novel coevolutionary model, in which each individual selects an action from a binary set and has an opinion on which action they prefer. Actions and opinions coevolve on a two-layer network. For homogeneous parameters, undirected networks, and under reasonable assumptions on the asynchronous updating mechanics, we prove that the coevolutionary dynamics is an ordinal potential game, enabling analysis via potential game theory. Specifically, we establish global convergence to the Nash equilibria of the game, proving that actions converge in a finite number of time steps, while opinions converge asymptotically. Next, we provide sufficient conditions for the existence of, and convergence to, polarized equilibria, whereby the population splits into two communities, each selecting and supporting one of the actions. Finally, we use simulations to examine the social psychological phenomenon of pluralistic ignorance.

Our group has been developing and operating a platform to acquire, archive, and manage various data related to the Earth's environment to make it available to researchers across a wide range of fields. Development of this system began in the 1980s to receive, archive, and distribute Asian satellite image data. Currently, the system covers a variety of data including weather, climate change, disaster prevention, biodiversity, health, and agriculture. Today, the Data Integration and Analysis System (DIASa) is a large-scale analysis platform with huge storage and more than 10,000 registered users (half of them in Japan and the other half primarily in Asia). As shown in the accompanying figure, users can easily use data collected by the common collection API through the common use API, and they can operate services at the application layer.

Is it possible for a developing country like Vietnam to be a competitive player on the world stage in cutting-edge artificial intelligence (AI) research and development? Will it be able to tap into the $US15.7 trillion projected for the AI global economy by 2030? For Vietnam, these questions often went unchallenged; contemplating answers was daunting. VinAI Research, however, aims to embrace these challenges by laying the groundwork for AI innovation and growth for the region. Founded in 2019, VinAI leapfrogged to the 20th ranking on Thundermark Capital's list of "Global AI Research Companies" by 2022, and was the only Southeast Asian (SEA) representative on the list.a

The New Zealand government identified numerous challenges related to trust and truth in the context of digital technologies. These challenges result from an ever-increasing amount of online social networks, end-to-end digital supply chains, automated decision-making tools, generative artificial intelligence (AI), and cyber-physical systems. Such challenges impact people's lives across professional and private contexts and led to the Veracity Projecta 2021–2024. Outside the field of computing, veracity is not a common term in everyday language. One dictionary definition is "conformity with truth or fact."b

For the world to realize the benefits brought by AI, it is important to ensure artificial intelligent (AI) systems are responsibly developed, used throughout their entire life cycle, and trusted by the humans expected to rely on them.1 The goal for AI adoption has triggered a significant national effort to realize responsible AI (RAI) in Australia. CSIRO Data61 is the data and digital specialist arm of Australia's national science agency. In 2019, CSIRO Data61's worked with the Australian government to conduct the AI Ethics Framework research. This work led to the release of eight AI ethics principles to ensure Australia's adoption of AI is safe, secure, and reliable.a It is challenging to turn high-level AI ethics principles into real-life practices.

The security of computer systems typically relies on a hardware root of trust. As vulnerabilities in hardware can have severe implications on a system, there is a need for techniques to support security verification activities. Assertion-based verification is a popular verification technique that involves capturing design intent in a set of assertions that can be used in formal verification or testing-based checking. However, writing security-centric assertions is a challenging task. In this work, we investigate the use of emerging large language models (LLMs) for code generation in hardware assertion generation for security, where primarily natural language prompts, such as those one would see as code comments in assertion files, are used to produce SystemVerilog assertions. We focus our attention on a popular LLM and characterize its ability to write assertions out of the box, given varying levels of detail in the prompt. We design an evaluation framework that generates a variety of prompts, and we create a benchmark suite comprising real-world hardware designs and corresponding golden reference assertions that we want to generate with the LLM.

Inspired by Solomonoffs theory of inductive inference, we propose a prior based on circuit complexity. There are several advantages to this approach. First, it relies on a complexity measure that does not depend on the choice of UTM. There is one universal definition for Boolean circuits involving an universal operation such as nand with simple conversions to alternative definitions such as and, or, and not. Second, there is no analogue of the halting problem. The output value of a circuit can be calculated recursively by computer in time proportional to the number of gates, while a short program may run for a very long time. Our prior assumes that a Boolean function, or equivalently, Boolean string of fixed length, is generated by some Bayesian mixture of circuits. This model is appropriate for learning Boolean functions from partial information, a problem often encountered within machine learning as "binary classification." We argue that an inductive bias towards simple explanations as measured by circuit complexity is appropriate for this problem.

We describe the systems of the University of Alberta team for the SemEval-2023 Visual Word Sense Disambiguation (V-WSD) Task. We present a novel algorithm that leverages glosses retrieved from BabelNet, in combination with text and image encoders. Furthermore, we compare language-specific encoders against the application of English encoders to translated texts. As the contexts given in the task datasets are extremely short, we also experiment with augmenting these contexts with descriptions generated by a language model. This yields substantial improvements in accuracy. We describe and evaluate additional V-WSD methods which use image generation and text-conditioned image segmentation. Overall, the results of our official submission rank us 18 out of 56 teams. Some of our unofficial results are even better than the official ones. Our code is publicly available at https://github.com/UAlberta-NLP/v-wsd.