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

NLP Highlights

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Insights Unlocked

1
How Anthropologie gets omnichannel right (and what to learn) 27:29

14 days ago27:29

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27:29

Episode web page: https://tinyurl.com/2b3dz2z8 ----------------------- Rate Insights Unlocked and write a review If you appreciate Insights Unlocked , please give it a rating and a review. Visit Apple Podcasts, pull up the Insights Unlocked show page and scroll to the bottom of the screen. Below the trailers, you'll find Ratings and Reviews. Click on a star rating. Scroll down past the highlighted review and click on "Write a Review." You'll make my day. ----------------------- In this episode of Insights Unlocked , we explore the evolving landscape of omnichannel strategies with Kate MacCabe, founder of Flywheel Strategy. With nearly two decades of experience in digital strategy and product management, Kate shares her insights on bridging internal silos, leveraging customer insights, and designing omnichannel experiences that truly resonate. From the early days of DTC growth to today’s complex, multi-touchpoint customer journeys, Kate explains why omnichannel is no longer optional—it’s essential. She highlights a standout example from Anthropologie, demonstrating how brands can create a unified customer experience across digital and physical spaces. Whether you’re a marketing leader, UX strategist, or product manager, this episode is packed with actionable advice on aligning teams, integrating user feedback, and building a future-proof omnichannel strategy. Key Takeaways: ✅ Omnichannel vs. Multichannel: Many brands think they’re omnichannel, but they’re really just multichannel. Kate breaks down the difference and how to shift toward true integration. ✅ Anthropologie’s Success Story: Learn how this brand seamlessly blended physical and digital experiences to create a memorable, data-driven customer journey. ✅ User Feedback is the Secret Weapon: Discover how continuous user testing—before, during, and after a launch—helps brands fine-tune their strategies and avoid costly mistakes. ✅ Aligning Teams for Success: Cross-functional collaboration is critical. Kate shares tips on breaking down silos between marketing, product, and development teams. ✅ Emerging Tech & Omnichannel: Instead of chasing the latest tech trends, Kate advises businesses to define their strategic goals first—then leverage AI, AR, and other innovations to enhance the customer experience. Quotes from the Episode: 💬 "Omnichannel isn’t just about being everywhere; it’s about creating seamless bridges between every touchpoint a customer interacts with." – Kate MacCabe 💬 "Companies that truly listen to their users—through qualitative and quantitative insights—are the ones that thrive in today’s competitive landscape." – Kate MacCabe Resources & Links: 🔗 Learn more about Flywheel Strategy 🔗 Connect with Kate MacCabe on LinkedIn 🔗 Explore UserTesting for customer insights for marketers…

2 years ago 29:36

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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

145 episod

#Artificial Intelligence #Tech #Science #NLP Highlights #Allen Institute for Artificial Intelligence #Tell Us

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

NLP Highlights

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published 2 years ago

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145 episod

#Artificial Intelligence #Tech #Science #NLP Highlights #Allen Institute for Artificial Intelligence #Tell Us

All episodes

1
Are LLMs safe? 42:15

1 year ago42:15

42:15

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!

1
"Imaginative AI" with Mohamed Elhoseiny 23:19

1 year ago23:19

23:19

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.…

1
142 - Science Of Science, with Kyle Lo 48:57

1 year ago48:57

48:57

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/…

1
141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld 29:36

2 years ago29:36

29:36

1
140 - Generative AI and Copyright, with Chris Callison-Burch 51:28

2 years ago51:28

51:28

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.…

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139 - Coherent Long Story Generation, with Kevin Yang 45:18

2 years ago45:18

45:18

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 discussed the challenges involved in evaluating long generated texts. Kevin Yang is a PhD student at UC Berkeley. Kevin's webpage: https://people.eecs.berkeley.edu/~yangk/ Papers discussed in this episode: 1. Re3: Generating Longer Stories With Recursive Reprompting and Revision (https://www.semanticscholar.org/paper/Re3%3A-Generating-Longer-Stories-With-Recursive-and-Yang-Peng/2aab6ca1a8dae3f3db6d248231ac3fa4e222b30a) 2. DOC: Improving Long Story Coherence With Detailed Outline Control (https://www.semanticscholar.org/paper/DOC%3A-Improving-Long-Story-Coherence-With-Detailed-Yang-Klein/ef6c768f23f86c4aa59f7e859ca6ffc1392966ca)…

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138 - Compositional Generalization in Neural Networks, with Najoung Kim 48:22

2 years ago48:22

48:22

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.…

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137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal 35:56

2 years ago35:56

35:56

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 memorization and generalization in language models. Urvashi's webpage: https://urvashik.github.io Papers discussed: 1) Generalization through memorization: Nearest Neighbor Language Models (https://www.semanticscholar.org/paper/7be8c119dbe065c52125ee7716601751f3116844) 2)Nearest Neighbor Machine Translation (https://www.semanticscholar.org/paper/20d51f8e449b59c7e140f7a7eec9ab4d4d6f80ea)…

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136 - Including Signed Languages in NLP, with Kayo Yin and Malihe Alikhani 1:02:15

3 years ago1:02:15

1:02:15

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, representations, computational resources, SLP tasks, and suggestions for better design and implementation of SLP models.…

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135 - PhD Application Series: After Submitting Applications 36:53

3 years ago36:53

36:53

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 at the University of Michigan), Aishwarya Kamath (PhD student at NYU), and Sanjay Subramanian (PhD student at UC Berkeley). Homepages: - Aishwarya Kamath: https://ashkamath.github.io/ - Sanjay Subramanian: https://sanjayss34.github.io/ - Rada Mihalcea: https://web.eecs.umich.edu/~mihalcea/ The hosts for this episode are Alexis Ross and Nishant Subramani.…

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134 - PhD Application Series: PhDs in Europe versus the US, with Barbara Plank and Gonçalo Correia 38:29

3 years ago38:29

38:29

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 the main differences between pursuing a PhD in Europe and the US. We then talk about the application requirements for European PhD programs and factors to consider when deciding whether to apply in Europe or the US. We conclude by talking about the ELLIS PhD program, a relatively new program for PhD students that facilitates collaborations across Europe. ELLIS PhD program: https://ellis.eu/phd-postdoc (Application Deadline: November 15, 2021) Homepages: - Barbara Plank: https://bplank.github.io/ - Gonçalo Correia: https://goncalomcorreia.github.io/ The hosts for this episode are Alexis Ross and Zhaofeng Wu.…

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133 - PhD Application Series: Preparing Application Materials, with Nathan Schneider and Roma Patel 43:54

4 years ago43:54

43:54

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 go into the decision to apply for PhD programs and how to gain relevant experience. We then talk about the most important parts of an application, focusing particularly on how to write a strong statement of purpose and choose recommendation letter writers. Blog posts mentioned in this episode: - Nathan Schneider’s Advice on Statements of Purpose: https://nschneid.medium.com/inside-ph-d-admissions-what-readers-look-for-in-a-statement-of-purpose-3db4e6081f80 - Student Perspectives on Applying to NLP PhD Programs: https://blog.nelsonliu.me/2019/10/24/student-perspectives-on-applying-to-nlp-phd-programs/ Homepages: - Nathan Schneider: https://people.cs.georgetown.edu/nschneid/ - Roma Patel: http://cs.brown.edu/people/rpatel59/ The hosts for this episode are Alexis Ross and Nishant Subramani.…

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132 - Alexa Prize Socialbot Grand Challenge and Alquist 4.0, with Petr Marek 41:43

4 years ago41:43

41:43

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 outstanding challenges. Petr Marek is a PhD student at the Czech Technical University in Prague. More about the Alexa Prize challenges: https://developer.amazon.com/alexaprize Technical report on Alquist 4.0: https://arxiv.org/abs/2109.07968…

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131 - Opportunities and Barriers between HCI and NLP, with Nanna Inie and Leon Derczynski 46:54

4 years ago46:54

46:54

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 NLP researchers. This discussion is based on the following paper: https://aclanthology.org/2021.hcinlp-1.16/ Nanna Inie is a postdoctoral researcher and Leon Derczynski is an associate professor in CS at the IT University of Copenhagen. The hosts for this episode are Ana Marasović and Pradeep Dasigi.…

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130 - Linking human cognitive patterns to NLP Models, with Lisa Beinborn 44:02

4 years ago44:02

44:02

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 importance measures of different tokens in human reading comprehension. We discuss empirical results suggesting that eye-tracking signals correlate strongly with gradient-based saliency measures, but not attention, in NLP methods. We conclude with discussion of the implications of these findings, as well as avenues for future work. Papers discussed in this episode: Towards best practices for leveraging human language processing signals for natural language processing: https://api.semanticscholar.org/CorpusID:219309655 Relative Importance in Sentence Processing: https://api.semanticscholar.org/CorpusID:235358922 Lisa Beinborn’s webpage: https://beinborn.eu/ The hosts for this episode are Alexis Ross and Pradeep Dasigi.…

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