NLP Highlights

NLP Highlights

**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.

Episodes

Are LLMs safe?

February 29, 2024 • 42 mins
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!
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"Imaginative AI" with Mohamed Elhoseiny

January 8, 2024 • 23 mins
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, demonst...
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142 - Science Of Science, with Kyle Lo

December 27, 2023 • 48 mins
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 fiel...
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141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld

June 29, 2023 • 29 mins
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 OL...
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140 - Generative AI and Copyright, with Chris Callison-Burch

June 5, 2023 • 51 mins
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 o...
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139 - Coherent Long Story Generation, with Kevin Yang

March 24, 2023 • 45 mins
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 discusse...
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138 - Compositional Generalization in Neural Networks, with Najoung Kim

January 20, 2023 • 48 mins
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 ...
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137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal

January 13, 2023 • 35 mins
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 models with non-parametric distributions over the closest values in some data stores built from relevant data. Not only are these models shown to outperform the usual parametric language models, they also have important implications on ...
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136 - Including Signed Languages in NLP, with Kayo Yin and Malihe Alikhani

May 19, 2022 • 62 mins
In this episode, we talk with Kayo Yin, an incoming PhD at Berkeley, and Malihe Alikhani, an assistant professor at the University of Pittsburgh, about opportunities for the NLP community to contribute to Sign Language Processing (SLP). We talked about history and misconceptions about sign languages, high-level similarities and differences between spoken and sign languages, distinct linguistic features of signed languages, represen...
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135 - PhD Application Series: After Submitting Applications

March 2, 2022 • 36 mins
This episode is the third in our current series on PhD applications. We talk about what the PhD application process looks like after applications are submitted. We start with a general overview of the timeline, then talk about how to approach interviews and conversations with faculty, and finish by discussing the different factors to consider in deciding between programs. The guests for this episode are Rada Mihalcea (Professor a...
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134 - PhD Application Series: PhDs in Europe versus the US, with Barbara Plank and Gonçalo Correia

October 19, 2021 • 38 mins
This episode is the second in our current series on PhD applications. How do PhD programs in Europe differ from PhD programs in the US, and how should people decide between them? In this episode, we invite Barbara Plank (Professor at ITU, IT University of Copenhagen) and Gonçalo Correia (ELLIS PhD student at University of Lisbon and University of Amsterdam) to share their perspectives on this question. We start by talking about t...
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133 - PhD Application Series: Preparing Application Materials, with Nathan Schneider and Roma Patel

October 5, 2021 • 43 mins
This episode is the first in our current series on PhD applications. How should people prepare their applications to PhD programs in NLP? In this episode, we invite Nathan Schneider (Professor of Linguistics and Computer Science at Georgetown University) and Roma Patel (PhD student in Computer Science at Brown University) to share their perspectives on preparing application materials. We start by talking about what factors should...
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132 - Alexa Prize Socialbot Grand Challenge and Alquist 4.0, with Petr Marek

September 27, 2021 • 41 mins
In this episode, we discussed the Alexa Prize Socialbot Grand Challenge and this year's winning submission, Alquist 4.0, with Petr Marek, a member of the winning team. Petr gave us an overview of their submission, the design choices that led to them winning the competition, including combining a hardcoded dialog tree and a neural generator model and extracting implicit personal information about users from their responses, and some...
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131 - Opportunities and Barriers between HCI and NLP, with Nanna Inie and Leon Derczynski

August 20, 2021 • 46 mins
What can NLP researchers learn from Human Computer Interaction (HCI) research? We chatted with Nanna Inie and Leon Derczynski to find out. We discussed HCI's research processes including methods of inquiry, the data annotation processes used in HCI, and how they are different from NLP, and the cognitive methods used in HCI for qualitative error analyses. We also briefly talked about the opportunities the field of HCI presents for N...
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130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn

August 9, 2021 • 44 mins
In this episode, we talk with Lisa Beinborn, an assistant professor at Vrije Universiteit Amsterdam, about how to use human cognitive signals to improve and analyze NLP models. We start by discussing different kinds of cognitive signals—eye-tracking, EEG, MEG, and fMRI—and challenges associated with using them. We then turn to Lisa’s recent work connecting interpretability measures with eye-tracking data, which reflect the relative...
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129 - Transformers and Hierarchical Structure, with Shunyu Yao

July 2, 2021 • 35 mins
In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language. Bounded-depth hierarchical structure is thought to be a key feature of natural languages, motivating Shunyu and his coauthors to show that transformers can efficiently represent bounded-depth Dyck languages, which can be thought of as a formal model of the structure of natural languages. We went on to...
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128 - Dynamic Benchmarking, with Douwe Kiela

June 18, 2021 • 47 mins
We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynabench. Dynamic benchmarking tries to address the issue of many recent datasets getting solved with little progress being made towards solving the corresponding tasks. The idea is to involve models in the data collectio...
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127 - Masakhane and Participatory Research for African Languages, with Tosin Adewumi and Perez Ogayo

June 8, 2021 • 47 mins
We invited members of Masakhane, Tosin Adewumi and Perez Ogayo, to talk about their EMNLP Findings paper that discusses why typical research is limited for low-resourced NLP and how participatory research can help.   As a result of participatory research, Masakhane has many, many success stories: first datasets and benchmarks in African languages, first research on human evaluation specifically for MT for low-resource languages, e...
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126 - Optimizing Continuous Prompts for Generation, with Lisa Li

May 24, 2021 • 47 mins
We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation. Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-specific continuous vector, and to keep the pretrained transformer parameters frozen. We discussed how prefix tuning compares with finetuning and other efficient alternatives on two tasks in various experimental setti...
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125 - VQA for Real Users, with Danna Gurari

May 3, 2021 • 42 mins
How can we build Visual Question Answering systems for real users? For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind. We talked about the differences between the existing datasets, and Vizwiz, a dataset built by Gurari et al., and the resulting algorithmic changes. We also discussed the unsolved challenges in this field, and the new tasks they result ...
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