NLP Highlights20 Jan 2023

138 - Compositional Generalization in Neural Networks, with Najoung Kim

Compositional generalization refers to the capability of models to generalize to out-of-distribution instances by composing information obtained from the training data. In this episode we chatted with Najoung Kim, on how to explicitly evaluate specific kinds of compositional generalization in neural network models of language. Najoung described COGS, a dataset she built for this, some recent results in the space, and why we should be careful about interpreting the results given the current practice of pretraining models of lots of unlabeled text. Najoung's webpage: https://najoungkim.github.io/ Papers we discussed: 1. COGS: A Compositional Generalization Challenge Based on Semantic Interpretation (Kim et al., 2020): https://www.semanticscholar.org/paper/b20ddcbd239f3fa9acc603736ac2e4416302d074 2. Compositional Generalization Requires Compositional Parsers (Weissenhorn et al., 2022): https://www.semanticscholar.org/paper/557ebd17b7c7ac4e09bd167d7b8909b8d74d1153 3. Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models (Kim et al., 2022): https://www.semanticscholar.org/paper/8969ea3d254e149aebcfd1ffc8f46910d7cb160e Note that we referred to the final paper by an earlier name in the discussion.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(145)

Are LLMs safe?

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 A...

29 Feb 202442min

"Imaginative AI" with Mohamed Elhoseiny

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 ...

8 Jan 202423min

142 - Science Of Science, with Kyle Lo

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 ...

28 Des 202348min

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

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? ...

29 Jun 202329min

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

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 ...

6 Jun 202351min

139 - Coherent Long Story Generation, with Kevin Yang

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...

24 Mar 202345min

137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal

We invited Urvashi Khandelwal, a research scientist at Google Brain to talk about nearest neighbor language and machine translation models. These models interpolate parametric (conditional) language m...

13 Jan 202335min