Prasoon Bajpai

30 posts

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Prasoon Bajpai

Prasoon Bajpai

@prasNLP

Research @GoogleDeepMind | @lcs2lab | @IITDelhi

India Katılım Ekim 2024
186 Takip Edilen311 Takipçiler
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
I will attend #EMNLP2024 at Miami next week! If you are interested LLM explainability, formal reasoning and/or multilingual NLP, please DM me and connect😃. I'm ready for a☕ talk every day! Also, please find me on Nov 13th 10:30-12:00 at poster session 6!
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Varshita Kolipaka
Varshita Kolipaka@VarshitaKolipa1·
navigating the EPFO website is the real humanity's last exam
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
@SuryaDoesIt @GoogleDeepMind Hi Surya! Usually the applications roll out near year end. Following initial screenings, there is a series of interviews and evaluations.
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Joykirat
Joykirat@joykiratsingh·
I’m thrilled to share that I’ll be joining the University of North Carolina at Chapel Hill for my CS PhD this fall!! 🎓💙 @UNC I’ll be working with the amazing @mohitban47 at @uncnlp. Grateful to everyone who’s supported me, excited for this new chapter! 🚀
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
NAACL 2025 🚀 Presenting “Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models” Paper Link : arxiv.org/abs/2408.10151
Tanmoy Chakraborty@Tanmoy_Chak

Kicking off the year with a bang -- 4 papers accepted in prestigious venues this month! #ICLR2025 -- 𝐋𝐋𝐌 𝐜𝐨𝐦𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧: We introduce 𝐏𝐫𝐮𝐧𝐞𝐍𝐞𝐭, a novel, dataset-free policy learning approach to model pruning, achieving high compression efficiency and performance retention, demonstrated by compressing LLaMA-2-7B with over 80% zero-shot accuracy retention at a 30% compression ratio. @iclr_conf URL: shorturl.at/HEO7O #𝐍𝐀𝐀𝐂𝐋2025 -- 𝐈𝐧𝐯𝐞𝐬𝐭𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐦𝐮𝐥𝐭𝐢𝐥𝐢𝐧𝐠𝐮𝐚𝐥 𝐥𝐨𝐧𝐠-𝐜𝐨𝐧𝐭𝐞𝐱𝐭 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫 𝐢𝐧 𝐋𝐋𝐌𝐬: We introduce 𝐌𝐋𝐍𝐞𝐞𝐝𝐥𝐞, the first systematic evaluation of multilingual long-context retrieval in LLMs, revealing significant performance variations across languages and context positions, with insights to guide future evaluations. @naaclmeeting Preprint: lnkd.in/gtRAXjmh 𝐍𝐀𝐀𝐂𝐋'25 -- 𝐂𝐨𝐮𝐧𝐭𝐞𝐫𝐬𝐩𝐞𝐞𝐜𝐡 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐚𝐧𝐝 𝐦𝐞𝐭𝐫𝐢𝐜𝐬: We introduce 𝐂𝐒𝐄𝐯𝐚𝐥, a dataset for evaluating counterspeech across four dimensions and a prompt-based framework using auto-calibrated CoT, offering better alignment with human judgment than traditional metrics. @naaclmeeting 𝐍𝐚𝐭𝐮𝐫𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: In collaboration with AIIMS (All India Institute of Medical Sciences, New Delhi), NIMHANS, Bangalore and other NGOs, we wrote how GenAI can potentially empower multisectoral suicide prevention efforts, particularly in resource-constrained settings like India. @NatMachIntell

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Tanmoy Chakraborty
Tanmoy Chakraborty@Tanmoy_Chak·
🌟 𝐀 𝐍𝐞𝐰 T𝐞𝐱𝐭𝐛𝐨𝐨𝐤 -- 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 🌟 I am excited to share the release of my new textbook, 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘵𝘰 𝘓𝘢𝘳𝘨𝘦 𝘓𝘢𝘯𝘨𝘶𝘢𝘨𝘦 𝘔𝘰𝘥𝘦𝘭𝘴 (#LLMs) -- Perhaps the first textbook on LLMs. Target Audience: 👉 Students/beginners, Looking for a structured starting point to learn LLMs 👉 Teachers, planning to offer a course on LLMs 👉 Industry professional, seeking to deepen their understanding of LLMs Explore the Book: 🔗 Book Website: tanmoychak.com/llmbook/ 📑 Table of Contents: tanmoychak.com/llmbook/toc.pdf 🛒 Available on Amazon: amazon.in/dp/936386474X/ Enhance Your Learning Experience: 👉 Slides & Lecture Videos: Chapter-wise resources -- lcs2-iitd.github.io/ELL881-AIL821-… 👉 Exercises & Solutions: Practice with detailed chapter exercises (solutions available on request). 👉 Upcoming @nptel_official Course: Starting January 2025! Preview here: onlinecourses.nptel.ac.in/noc25_cs45/pre… Book Endorsement: 📖 Foreword by Prof. Tim Baldwin @eltimster 👏 Endorsements from Prof. Iryna Gurevych @IGurevych and Prof. Pushpak Bhattacharyya #LLMs #Textbook @iitdelhi @WileyIndiaPL @lcs2lab
Tanmoy Chakraborty tweet media
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
We also find that LLMs struggle to give proper attention to parts of queries, which are grounded in highly popular entities. Check out the full paper for more key insights, real-world implications and detailed methodology : arxiv.org/abs/2411.10813…
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
We also assess this impact critical limitation under the lens of sensitivity towards lexical variations of the queries. We unveil a key weakness in modern LLMs, in being internally sensitive to lexical perturbations, while retrieving highly popular facts from their memory.
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Prasoon Bajpai
Prasoon Bajpai@prasNLP·
“Beware the fury of the highly popular knowledge” Does highly popular information cause any internal struggle in LLMs? (1/n)
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Misha Khodak
Misha Khodak@khodakmoments·
🧵 on surprising revelations from our study of specialized foundation models (FMs beyond vision/text): after evaluating dozens of scientific & time series FMs we found that most weren’t even competitive with simple supervised models, some with as little as 513 parameters. 1/n
Misha Khodak tweet media
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