The LLM Revolution: Implications from Chatbots and Tool-use to Reasoning

The LLM (Large Language Model) Revolution: Implications from Chatbots and Tool-use to Reasoning

Deep learning is a shock to our field in many ways, yet still many of us were surprised at the incredible performance of Large Language Models (LLMs). LLM uses new deep learning techniques with massively large data sets to understand, predict, summarize, and generate new content. LLMs like ChatGPT and Bard have seen a dramatic increase in their capabilities---generating text that is nearly indistinguishable from human-written text, translating languages with amazing accuracy, and answering your questions in an informative way. This has led to a number of exciting research directions for chatbots, tool-use, and reasoning:

- Chatbots: LLM chatbots that are more engaging and informative than traditional chatbots. First, LLMs can understand the context of a conversation better than ever before, allowing them to provide more relevant and helpful responses. Second, LLMs enable more engaging conversations than traditional chatbots, because they can understand the nuances of human language and respond in a more natural way. For example, LLMs can make jokes, ask questions, and provide feedback. Finally, because LLM chatbots can hold conversations on a wide range of topics, they can eventually learn and adapt to the user's individual preferences.

- Tool-use, Retrieval Augmentation and Multi-modality: LLMs are also being used to create tools that help us with everyday tasks. For example, LLMs can be used to generate code, write emails, and even create presentations. Beyond human-like responses in Chatbots, later LLM innovators realized LLM’s ability to incorporate tool-use, including calling search and recommendation engines, which means that they could effectively become human assistants in synthesizing summaries from web search and recommendation results. Tool-use integration have also enabled multimodal capabilities, which means that the chatbot can produce text, speech, images, and video.

- Reasoning: LLMs are also being used to develop new AI systems that can reason and solve problems. Using Chain-of-Thought approaches, we have shown LLM's ability to break down problems, and then use logical reasoning to solve each of these smaller problems, and then combine the solutions to reach the final answer. LLMs can answer common-sense questions by using their knowledge of the world to reason about the problem, and then use their language skills to generate text that is both creative and informative.

In this talk, I will cover recent advances in these 3 major areas, attempting to draw connections between them, and paint a picture of where major advances might still come from. While the LLM revolution is still in its early stages, it has the potential to revolutionize the way we interact with AI, and make a significant impact on our lives.