Hosted by Allen Institute for Artificial Intelligence · 🇺🇸 US · EN · 145 episodes
Established thought leaders with verified media credentials.
**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.
Allen Institute for Artificial Intelligence hosts NLP Highlights, a science show with 145 episodes published.
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
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
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) o
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 qu
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
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
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
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-param
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 P
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
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
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
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 de
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 anno
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 c
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 language
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 Dynaben
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
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-sp
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
Sponsor detection runs nightly. Check back soon.
No public pitch examples yet for this show.
Generate your own personalised pitchBased on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:
Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.
Shows with the most semantically similar episode content. Pitch one, pitch all; producers cluster.
NLP Highlights has a verified contact on file. Create a free PitchCentric account to access it and generate a personalised pitch in seconds. Research at least 3 recent episodes first and lead with a specific angle that serves their science audience.
NLP Highlights is hosted by Allen Institute for Artificial Intelligence. The show is categorised under Science and has published 145 episodes.
NLP Highlights has published 145 episodes.
NLP Highlights regularly covers Science. It sits in the Science category.
NLP Highlights is accessible for guests with genuine science expertise. A personalised, episode-aware pitch will still outperform a generic one every time.
NLP Highlights hasn't explicitly signalled guest openness in recent episodes. That doesn't rule out pitching. your hook just needs to be especially compelling and relevant to their recent content.
Episodes of NLP Highlights average 43 minutes. a focused format where a clear narrative arc and tight preparation matter most.
Our data rates NLP Highlights's guest bar at 80/100 (Premium tier). Established thought leaders with verified media credentials. Sign in to PitchCentric to see how your own Pod Score compares against this show.
Methodology. Booking Probability™ blends Listen Score, 30-day Virality, open-to-guests detection, and Apple ratings. Data refreshed every 60 minutes. Listen Score and Booking Probability are calculated by PitchCentric. Last enriched 10 days ago.
