Adithya Bhaskar @AdithyaNLP

Second Year CS Ph.D. student at Princeton University (@princeton_nlp), previously CS undergrad at IIT Bombay adithyabh.github.io Princeton, NJ Joined June 2023

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CLS @ChengleiSi

2 months ago

Are AI scientists already better than human researchers? We recruited 43 PhD students to spend 3 months executing research ideas proposed by an LLM agent vs human experts. Main finding: LLM ideas result in worse projects than human ideas.

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Xi Ye @xiye_nlp

3 months ago

🤔 Recent mech interp work showed that retrieval heads can explain some long-context behavior. But can we use this insight for retrieval? 📣 Introducing QRHeads (query-focused retrieval heads) that enhance retrieval Main contributions: 🔍 Better head detection: we find a…

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Xindi Wu @cindy_x_wu

4 months ago

Introducing COMPACT: COMPositional Atomic-to-complex Visual Capability Tuning, a data-efficient approach to improve multimodal models on complex visual tasks without scaling data volume. 📦 arxiv.org/abs/2504.21850 1/10

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Xindi Wu @cindy_x_wu

8 months ago

Want to train large vision-language models but drowning in data? arxiv.org/abs/2501.00654 Introducing ICONS - we demonstrate how to select only 20% of training samples while maintaining 98.6% of the performance, and 60% of training samples to achieve 102.1% of the performance.

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Tyler Zhu @tyleryzhu

8 months ago

Have you ever wondered why we don’t use multiple visual encoders for VideoLLMs? We thought the same! Excited to announce our latest work MERV, on using Multiple Encoders for Representing Videos in VideoLLMs, outperforming prior works with the same data. 🧵

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Noam Razin @noamrazin

11 months ago

Past work observed that DPO often decreases the probability of preferred responses. So where does the probability go? 🧐 We investigate the causes for this counter-intuitive phenomenon and show that it can lead to surprising failures in alignment! 📰 arxiv.org/abs/2410.08847 🧵

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Adithya Bhaskar @AdithyaNLP

11 months ago

I’m at COLM from Monday to Wednesday. Reach out if you want to chat!

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Howard Yen @HowardYen1

a year ago

Come check out our poster at #ACL2024! I will be at the 4pm poster session, stop by to chat about long-context models

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Adithya Bhaskar @AdithyaNLP

a year ago

I'll be at ACL 2024! I'd love to chat with about interpretability, preference optimization, science of LM, or any NLP topics -- feel free to reach out! Oh, and I'll present The Heuristic Core (arxiv.org/abs/2403.03942) both as an oral (Aug 13 10:30) and a poster (Aug 12 14:00).

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Dan Friedman @danfriedman0

a year ago

How can we understand neural chatbots in terms of interpretable, symbolic mechanisms? To explore this question, we constructed a Transformer that implements the classic ELIZA chatbot algorithm (with @Abhishek_034 and @danqi_chen). Paper: arxiv.org/abs/2407.10949 (1/6)

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Sadhika Malladi @SadhikaMalladi

a year ago

My new blog post argues from first principles how length normalization in preference learning objectives (e.g., SimPO) can facilitate learning from model-annotated preference data. Check it out! cs.princeton.edu/~smalladi/blog…