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.

140 - Generative AI and Copyright, with Chris Callison-Burch

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

141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld

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/

142 - Science Of Science, with Kyle Lo

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.

"Imaginative AI" with Mohamed Elhoseiny

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!

Are LLMs safe?

What function do the different attention heads serve in multi-headed attention models?  In this episode, Lena describes how to use attribution methods to assess the importance and contribution of different heads in several tasks, and describes a gating mechanism to prune the number of effective heads used when combined with an auxiliary loss. Then, we discuss Lena’s work on studying the evolution of representations of individual tokens in transformers model.

Lena’s homepage:
https://lena-voita.github.io/

Blog posts:
https://lena-voita.github.io/posts/acl19_heads.html
https://lena-voita.github.io/posts/emnlp19_evolution.html

Papers:
https://arxiv.org/abs/1905.09418
https://arxiv.org/abs/1909.01380

98 - Analyzing Information Flow In Transformers, With Elena Voita

We talked to Brendan O’Connor for this episode about processing language in social media. Brendan started off by telling us about his projects that studied the linguistic and geographical patterns of African American English (AAE), and how obtaining data from Twitter made these projects possible. We then talked about how many tools built for standard English perform very poorly on AAE, and why collecting dialect-specific data is important. For the rest of the conversation, we discussed the issues involved in scraping data from social media, including ethical considerations and the biases that the data comes with.

Brendan O’Connor is an Assistant Professor at the University of Massachusetts, Amherst.

Warning: This episode contains explicit language (one swear word).

103 - Processing Language in Social Media, with Brendan O'Connor

In this episode, we talked to Emily Bender about the ethical considerations in developing NLP models and putting them in production. Emily cited specific examples of ethical issues, and talked about the kinds of potential concerns to keep in mind, both when releasing NLP models that will be used by real people, and also while conducting NLP research. We concluded by discussing a set of open-ended questions about designing tasks, collecting data, and publishing results, that Emily has put together towards addressing these concerns.

Emily M. Bender is a Professor in the Department of Linguistics and an Adjunct Professor in the Department of Computer Science and Engineering at the University of Washington. She’s active on Twitter at @emilymbender.

106 - Ethical Considerations In NLP Research, with Emily Bender

In this episode we invite Verena Rieser and Ondřej Dušek on to talk to us about the complexities of generating natural language when you have some kind of structured meaning representation as input.  We talk about when you might want to do this, which is often is some kind of a dialog system, but also generating game summaries, and even some language modeling work.  We then talk about why this is hard, which in large part is due to the difficulty of collecting data, and how to evaluate the output of these systems.

We then move on to discussing the details of a major challenge that Verena and Ondřej put on, called the end-to-end natural language generation challenge (E2E NLG). This was a dataset of task-based dialog generation focused on the restaurant domain, with some very innovative data collection techniques.  They held a shared task with 16 participating teams in 2017, and the data has been further used since.  We talk about the methods that people used for the task, and what we can learn today from what methods have been used on this data.

Verena's website: https://sites.google.com/site/verenateresarieser/
Ondřej's website: https://tuetschek.github.io/
The E2E NLG Challenge that we talked about quite a bit: http://www.macs.hw.ac.uk/InteractionLab/E2E/

108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek

How do we know, in a concrete quantitative sense, what a deep learning model knows about language? In this episode, Ellie Pavlick talks about two broad directions to address this question: structural and behavioral analysis of models. In structural analysis, we often train a linear classifier for some linguistic phenomenon we'd like to probe (e.g., syntactic dependencies) while using the (frozen) weights of a model pre-trained on some tasks (e.g., masked language models). What can we conclude from the results of probing experiments? What does probing tell us about the linguistic abstractions encoded in each layer of an end-to-end pre-trained model? How well does it match classical NLP pipelines? How important is it to freeze the pre-trained weights in probing experiments? In contrast, behavioral analysis evaluates a model's ability to distinguish between inputs which respect vs. violate a linguistic phenomenon using acceptability or entailment tasks, e.g., can the model predict which is more likely: "dog bites man" vs. "man bites dog"? We discuss the significance of which format to use for behavioral tasks, and how easy it is for humans to perform such tasks.

Ellie Pavlick's homepage: 
https://cs.brown.edu/people/epavlick/

BERT rediscovers the classical nlp pipeline , by Ian Tenney, Dipanjan Das, Ellie Pavlick
https://arxiv.org/pdf/1905.05950.pdf?fbclid=IwAR3gzFibSBoDGdjqVu9Gq0mh1lDdRZa7dm42JuXXUfjG6rKZ44iHIOdV6jg

Inherent Disagreements in Human Textual Inferences by Ellie Pavlick and Tom Kwiatkowski 
https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00293

109 - What Does Your Model Know About Language, with Ellie Pavlick

**The podcast is currently on hiatus. For more active NLP content, check out the Holistic Intelligence Podcast linked below.**

Welcome to the NLP highlights podcast, where we invite researchers to talk about their work in various areas in natural language processing. All views expressed belong to the hosts/guests, and do not represent their employers.

108 - Data-To-Text Generation, with Verena Rieser and Ondřej Dušek

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