Building on this, we will examine joint text-speech pre-training (Chiu et al., 2022; Bar-rault et al., 2023; Chen et al., 2022) methods, This section will provide a comprehensive look at how state-of-the-art voice-interfaced LLMs (Reid et al., 2024; Chu et al., Current Trends The current work in AI virtual assistants builds upon the voice-only systems of the last decade by leveraging LLMs to significantly improve the coverage and robustness of the spoken language understanding and dialogue state tracking components, in addition to substantial advancements in spoken language generation. It highlights recent advancements in multi-turn dialogue systems, encompassing both LLM-based open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems, as well as relevant datasets and evaluation metrics.

Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge. A particularly promising direction is to augment and ground LLMs with information from structured sources. This paper introduces Conversational Tables (cTBLS), a three-step architecture to retrieve and generate dialogue responses grounded on retrieved tabular information. cTBLS uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 125% relative improvement over the retriever in the previous state-of-the-art system on the HyrbiDialogue dataset. cTBLS then uses a shared process between encoder and decoder models to perform a coarse+fine tabular knowledge (e.g., cell) ranking combined with a GPT-3.5 LLM response generator to yield a 2x relative improvement in ROUGE scores. Finally, human evaluators prefer cTBLs +80% of the time (coherency, fluency) and judge informativeness to be 4x better than the previous state-of-the-art.

Located in Toronto's downtown core at MaRS Discovery District, the new Samsung AI Centre will contribute to building the connected future by accelerating the adoption of intelligence on multiple devices ranging from household appliances to cars. The Toronto AI Centre is a part of a network of research Centres dedicated to research and development in the field of AI. The Centre is the second Samsung AI Centre to be established in North America, with the other in Mountain View, California. The North America AI Centres are led by senior vice president, Dr. Larry Heck, a renowned expert in machine learning for spoken and text language processing, who also co-leads the expansion of Samsung's AI Centres around the globe. "Toronto and the GTA are epi-centres of machine learning and one of the world's foremost hubs for AI research and development. Home to not only world-class talent, but also some of the most innovative start-ups in the artificial intelligence field," said Dr. Larry Heck, Co-Head of Global Artificial Intelligence Research.