Abstract Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writing such documents provide great benefits but are challenging to design.[…]
Abstract Current Natural Language Processing systems rely heavily on Large Language Models (LLMs) which are trained rather naively on large amounts of text using autoregressive next token prediction. Often, they are fine-tuned to mimic human[…]
Abstract While large language models (LLMs) have revolutionized AI, their token-level processing contrasts sharply with human cognition’s multi-level abstraction. This talk explores moving beyond token-based manipulation to reason in a latent space. We introduce the[…]
Abstract This is a casual discussion with Oren Etzioni and will touch upon various topics, including the advancement of AI, its effects on society and politics and entrepreneurship in today’s technological era. There will also be[…]
Abstract How do humans efficiently learn new rules, causal laws, and mental algorithms, and how could AI systems do the same? From the perspective of human behavior, I will present results suggesting that representing knowledge[…]
Abstract Recent advances in large language models (LLMs) have achieved remarkable results across a wide range of natural language processing (NLP) applications, including text classification, summarization, machine translation, and dialogue systems. As LLMs grow increasingly[…]
Abstract Over the past decades, machine learning has primarily relied on labeled data, with success often depending on the availability of vast, high-quality annotations and the assumption that test conditions mirror training conditions. In contrast,[…]
