NLIP Seminar Series

Abstract not available

Abstract not available

This talk introduces Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed by authoritative sources like the IPCC reports and peer-reviewed scientific literature, Climinator employs an innovative Mediator-Advocate...

The field of NLP is in the midst of a disruptive shift, fueled most recently by the advent of large language models (LLMs), with impacts on our methodologies, funding and public perception. While the core technologies and scope of real-world impact of our field may be changing (everything is different!), many of the same...

An overview of the mis/disinformation space, a framework for breaking it down into meaningful problems, and a look at some of the new tools and technology coming from Google to support fact-checkers and expert users around the world. Bio: Mevan Babakar is the News and Information Credibility Lead at Google, working to...

In this talk, Alexander will cover from different perspectives how search systems can respond to comparative questions, which was the main focus of his PhD studies. He will discuss approaches to identifying comparative questions and identifying important constituents of comparative questions, such as the options being...

Neural models -- including language models such as ChatGPT -- can exhibit remarkable abilities; paradoxically, they also struggle with algorithmic tasks where much simpler models excel. To solve these issues, we propose Implicit Maximum Likelihood Estimation (IMLE), a framework for end-to-end learning of models combining...

My research focuses on the cognitive plausibility of NLP models and I will discuss two examples of using human processing data for analyzing language processing in computational models. We look into similarities and differences in representing the input and in the sensitivity to structural complexity. Our research...

Abstract: The availability of large, high-quality datasets has been one of the main drivers of recent progress in generation tasks like summarization, QA. Such annotated datasets however are difficult and costly to collect, and rarely exist in languages other than English, rendering the technology inaccessible to...

Standard probabilistic language generators often fall short when it comes to producing coherent and fluent text despite the fact that the underlying models perform well under standard metrics, e.g., perplexity. This discrepancy has puzzled the language generation community for the last few years. In this talk, we’ll take a...