catch the inductive bias that was captured by the larger model

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?

ACL 2017 paper by Abigail See, Peter Liu, and Chris Manning.

Matt presents the paper, describing the task (summarization on CNN/Daily Mail), the model (the standard copy + generate model that people are using these days, plus a nice coverage loss term), and the results (can't beat the extractive baseline, but coming close).  It's a nice paper - very well written, interesting discussion section.

https://www.semanticscholar.org/paper/Get-To-The-Point-Summarization-with-Pointer-Genera-See-Liu/13db673d09f546698e0bfb6687beeb5345f81ad9

Abigail also has a very nice blog post where she describes her work in a less formal tone than the paper: http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

23 - Get To The Point: Summarization with Pointer-Generator Networks

NIPS 2017 paper.

We dig into the details of the Transformer, from the "attention is all you need" paper.  Ashish and Jakob give us some motivation for replacing RNNs and CNNs with a more parallelizable self-attention mechanism, they describe how this mechanism works, and then we spend the bulk of the episode trying to get their intuitions for _why_ it works.  We discuss the positional encoding mechanism, multi-headed attention, trying to use these ideas to replace encoders in other models, and what the self-attention actually learns.  Turns out that the lower layers learn something like n-grams (similar to CNNs), and the higher layers learn more semantic-y things, like coreference.

https://www.semanticscholar.org/paper/Attention-Is-All-You-Need-Vaswani-Shazeer/0737da0767d77606169cbf4187b83e1ab62f6077

Minor correction: Talking about complexity equations without the paper in front of you can be tricky, and Ashish and Jakob may have gotten some of the details slightly wrong when we were discussing computational complexity.  The high-level point is that self-attention is cheaper than RNNs when the hidden dimension is higher than the sequence length.  See the paper for more details.

36 - Attention Is All You Need, with Ashish Vaswani and Jakob Uszkoreit

ACL 2017 paper, by Paramita Mirza, Simon Razniewski, Fariz Darari, and Gerhard Weikum.

There's not a whole lot of work on numbers in NLP, and getting good information out of numbers expressed in text can be challenging.  In this episode, Paramita comes on to tell us about her efforts to use distant supervision to learn models that extract relation cardinalities from text.  That is, given an entity and a relation in a knowledge base, like "Barack Obama" and "has child", the goal is to extract _how many_ related entities there are (in this case, two).  There are a lot of challenges in getting this to work well, and Paramita describes some of those, and how she solved them.

https://www.semanticscholar.org/paper/Cardinal-Virtues-Extracting-Relation-Cardinalities-Mirza-Razniewski/01afba9f40e0df06446b9cd3d5ea8725c4ba1342

50 - Cardinal Virtues: Extracting Relation Cardinalities from Text, with Paramita Mirza

NAACL 2018 paper, by Matt Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Chris Clark, Kenton Lee, and Luke Zettlemoyer.

In this episode, AI2's own Matt Peters comes on the show to talk about his recent work on ELMo embeddings, what some have called "the next word2vec".  Matt has shown very convincingly that using a pre-trained bidirectional language model to get contextualized word representations performs substantially better than using static word vectors.  He comes on the show to give us some more intuition about how and why this works, and to talk about some of the other things he tried and what's coming next.

https://www.semanticscholar.org/paper/Deep-contextualized-word-representations-Peters-Neumann/4b17597b856c087f109381ce77d60d9017cb6f9a

NLP Highlights

56 - Deep contextualized word representations, with Matthew Peters

Upcoming JAIR paper by Sebastian Ruder, Ivan Vulić, and Anders Søgaard.

Sebastian comes on to tell us about his survey.  He creates a typology of cross-lingual word embedding methods, and we discuss why you might use cross-lingual embeddings (low-resource languages in particular), what information they capture (semantics? syntax? both?), how the methods work (lots of different ways), and how to evaluate the embeddings (best when you have an extrinsic task to evaluate on).

https://www.semanticscholar.org/paper/A-survey-of-cross-lingual-embedding-models-Ruder/3dbd28c63a7807280c9531735c715d4598024166

Episode 57_Sebastian Ruder

57 - A Survey Of Cross-lingual Word Embedding Models, with Sebastian Ruder

In this episode we talked with Victor Sanh and Thomas Wolf from HuggingFace about model distillation, and DistilBERT as one example of distillation. The idea behind model distillation is compressing a large model by building a smaller model, with much fewer parameters, that approximates the output distribution of the original model, typically for increased efficiency. We discussed how model distillation was typically done previously, and then focused on the specifics of DistilBERT, including training objective, empirical results, ablations etc. We finally discussed what kinds of information you might lose when doing model distillation.

104 - Model Distillation, with Victor Sanh and Thomas Wolf

**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.

104 - Model Distillation, with Victor Sanh and Thomas Wolf

NLP Highlights

Recent comments

julien_c

Related tracks