NLP Highlights Allen Institute for Artificial Intelligence

Curious about the safety of LLMs? 🤔 Join us for an insightful new episode featuring Suchin Gururangan, Young Investigator at Allen Institute for Artificial Intelligence and Data Science Engineer at Appuri. 🚀 Don't miss out on expert insights into the world of LLMs!

This podcast episode features Dr. Mohamed Elhoseiny, a true luminary in the realm of computer vision with over a decade of groundbreaking research. As an Assistant Professor at KAUST, Dr. Elhoseiny's work delves into the intersections of Computer Vision, Language & Vision, and Computational Creativity in Art, Fashion, and AI. Notably, he co-organized the 1st and 2nd Workshops on Closing the Loop between Vision and Language, demonstrating his commitment to advancing interdisciplinary research. With a rich educational background from Stanford University's Graduate School of Business Ignite Program, and Rutgers University as MS/PhD Researcher, coupled with influential stints at Stanford, Baidu Research, Facebook AI Research, Adobe Research, and SRI International, Dr. Elhoseiny brings a wealth of experience to our discussion.

Our first guest with this new format is Kyle Lo, the most senior lead scientist in the Semantic Scholar team at Allen Institute for AI (AI2), who kindly agreed to share his perspective on #Science of #Science (#scisci) on our podcast. SciSci is concerned with studying how people do science, and includes developing methods and tools to help people consume AND produce science. Kyle has made several critical contributions in this field which enabled a lot of SciSci work over the past 5+ years, ranging from novel NLP methods (eg, SciBERT https://lnkd.in/gTP_tYiF ), to open data collections (eg, S2ORK https://lnkd.in/g4J6tXCG), to toolkits for manipulating scientific documents (eg, PaperMage https://lnkd.in/gwU7k6mJ which JUST received a Best Paper Award 🏆 at EMNLP 2023).

Kyle Lo's homepage: https://kyleclo.github.io/

In this special episode of NLP Highlights, we discussed building and open sourcing language models. What is the usual recipe for building large language models? What does it mean to open source them? What new research questions can we answer by open sourcing them? We particularly focused on the ongoing Open Language Model (OLMo) project at AI2, and invited Iz Beltagy and Dirk Groeneveld, the research and engineering leads of the OLMo project to chat.

Blog post announcing OLMo: https://blog.allenai.org/announcing-ai2-olmo-an-open-language-model-made-by-scientists-for-scientists-ab761e4e9b76

Organizations interested in partnership can express their interest here: https://share.hsforms.com/1blFWEWJ2SsysSXFUEJsxuA3ioxm

You can find Iz at twitter.com/i_beltagy and Dirk at twitter.com/mechanicaldirk

In this special episode, we chatted with Chris Callison-Burch about his testimony in the recent U.S. Congress Hearing on the Interoperability of AI and Copyright Law. We started by asking Chris about the purpose and the structure of this hearing. Then we talked about the ongoing discussion on how the copyright law is applicable to content generated by AI systems, the potential risks generative AI poses to artists, and Chris’ take on all of this. We end the episode with a recording of Chris’ opening statement at the hearing.

How can we generate coherent long stories from language models? Ensuring that the generated story has long range consistency and that it conforms to a high level plan is typically challenging. In this episode, Kevin Yang describes their system that prompts language models to first generate an outline, and iteratively generate the story while following the outline and reranking and editing the outputs for coherence. We also discussed the challenges involved in evaluating long generated texts.

Kevin Yang is a PhD student at UC Berkeley.

Kevin's webpage: https://people.eecs.berkeley.edu/~yangk/

Papers discussed in this episode:
1. Re3: Generating Longer Stories With Recursive Reprompting and Revision (https://www.semanticscholar.org/paper/Re3%3A-Generating-Longer-Stories-With-Recursive-and-Yang-Peng/2aab6ca1a8dae3f3db6d248231ac3fa4e222b30a)
2. DOC: Improving Long Story Coherence With Detailed Outline Control (https://www.semanticscholar.org/paper/DOC%3A-Improving-Long-Story-Coherence-With-Detailed-Yang-Klein/ef6c768f23f86c4aa59f7e859ca6ffc1392966ca)