@hamidpalangi Profile picture

Hamid Palangi

@hamidpalangi

Staff Research Scientist @Google, Affiliate Associate Professor @UW

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I am seeking student researchers to hire for 2025 focusing on Multi-Agent LLMs and Inference Time Decoding Algorithms. Ideal candidates are last year PhD students with relevant publications. If interested, please email your CV to hamidpalangi@google.com Thank you!


Tom is great to work with!

📢 I am recruiting PhD students in Data Science at UVA to start Fall 2025! Come join a wonderful group working on Responsible ML/NLP in Ever-Changing Environments🎉 More info on my website: tomhartvigsen.com/research-group

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Hamid Palangi Reposted

** Intern position on LLM reasoning ** @mchorton1991, @i_mirzadeh, @KeivanAlizadeh2 and I are co-hosting an intern position at #Apple to work on understanding and improving reasoning capabilities of LLMs. The ideal candidate: - Has prior publications on LLM reasoning - Is…

1/ Can Large Language Models (LLMs) truly reason? Or are they just sophisticated pattern matchers? In our latest preprint, we explore this key question through a large-scale study of both open-source like Llama, Phi, Gemma, and Mistral and leading closed models, including the…

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Excited to have you Mihir!

🎉 Super Excited to join the @Google as an Intern! Looking forward to gaining this new experience and working on impactful projects in NLP. Thank you so much @hmd_palangi for this opportunity! #Interning #NLProc #AIResearch

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Hamid Palangi Reposted

If you are interested in being co-supervised by me and and Sarath Chandar, consider applying here.

Thanks to the agreement between @CIFAR_News and @ELLISforEurope, I will recruit a PhD student through the ELLIS program for Fall 2025. I plan to co-supervise with the amazing @caglarml, and the student will split their time between @Mila_Quebec and @EPFL_en! Deadline: Nov 15!



Hamid Palangi Reposted

👀 How to find a better adapted model? ✨ Let the models find it for you! 👉🏻 Introducing Model Swarms, multiple LLM experts collaboratively search for new adapted models in the weight space and discover their new capabilities. 📄 Paper: arxiv.org/abs/2410.11163

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Dr. Milanfar is doing fundamental work, I have been inspired by his work since years ago when I was a graduate student.

Our team @ Google is hiring 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 to develop cutting-edge image & video enhancement solutions. Focus is on research & application of generative models to real imaging problems Qualified candidates only (incl. new PhDs): pls DM me/@2ptmvd with CV

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I am seeking student researchers to hire for Fall focusing on Reliability and Steerability of Large Language Models. Ideal candidates are last year PhD students with relevant publications. If interested, please email your CV to hamidpalangi@google.com Thank you!


Hamid Palangi Reposted

What happens when LLMs encounter information that contradicts their static knowledge? 🤔 Discover our findings, including a new dataset and interpretability method, in our ACL 2024 paper! 🧵👇 📄 Read the paper: arxiv.org/abs/2312.02073 🖥️ Explore more: epfl-dlab.github.io/llm-grounding-…

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If you are attending #ICLR2024 and interested in grounding/fabrication mitigation techniques for SLMs using synthetic *tasks* please stop by tomorrow 10:45-12:45 at Hall B, this is wonderful work led by @ErikJones313 during his internship at @MSFTResearch: openreview.net/pdf?id=xpw7V0P…


Hamid Palangi Reposted

Happy birthday Joseph Fourier, whose 1822 equation allows us to listen to mp3s today: bit.ly/22kbNfi

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Hamid Palangi Reposted

Another example of how simple things work

🚨 Reverse Training to Nurse the Reversal Curse🚨 LLMs fail on “B is A” if only trained on "A is B". - Reverse training doubles training tokens by reversing strings - Outperforms data-matched standard baselines - Fixes issues on reversal tasks arxiv.org/pdf/2403.13799… 🧵(1/6)

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Hamid Palangi Reposted

When we started building a safety-focused LLM for healthcare a year back, a result like this was beyond imagination. We are excited to share some of the technical and a lot of the clinical considerations that went into building #Polaris in our 53-page technical report available…

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Hamid Palangi Reposted

Another small update from us, including some fun results about circuit analysis with SAEs.

A small update from the Anthropic interpretability team: transformer-circuits.pub/2024/march-upd…



Hamid Palangi Reposted

We're announcing TacticAI: an AI assistant capable of offering insights to football experts on corner kicks. ⚽ Developed with @LFC, it can help teams sample alternative player setups to evaluate possible outcomes, and achieves state-of-the-art results. 🧵…


Hamid Palangi Reposted

📢 The call for #CoLLAs2024 tutorials is OUT! 📢 🗓️ Tutorial proposals should be submitted to tutorials@lifelong-ml.cc by April 28, 2024, 11:59 PM AOE. More details 👇 lifelong-ml.cc/Conferences/20…


Hamid Palangi Reposted

Today we're releasing a tool we've been using internally to analyze transformer internals - the Transformer Debugger! It combines both automated interpretability and sparse autoencoders, and it allows rapid exploration of models without writing code.


Hamid Palangi Reposted

Today we're excited to introduce Devin, the first AI software engineer. Devin is the new state-of-the-art on the SWE-Bench coding benchmark, has successfully passed practical engineering interviews from leading AI companies, and has even completed real jobs on Upwork. Devin is…


Hamid Palangi Reposted

@CVPR 2024 workshop on Responsible Generative AI deadline is extended to March 25! Submit any relevant work on the intersection of Generative AI and Fairness/Responsible AI (@FAccTConference @WiMLworkshop @WiCVworkshop)

We’ve extended the deadline of the @ReGenAIWorkshop at #CVPR2024 to 💥March 25th💥. If you are working at the intersection of gen AI, vision, ethics, privacy and regulation, consider submitting your work! More info here: sites.google.com/view/cvpr-resp…



Hamid Palangi Reposted

Introducing Orca-Math, our Mistral-7B offshoot excelling in math word problems! 🧮🐳 - Impressive 86.81% score on GSM8k - Surpasses models 10x larger or with 10x more training data - No code, verifiers, or ensembling tricks needed

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