🚀Very excited about my new paper! NN-CIFT slashes data valuation costs by 99% using tiny neural nets (205k params, just 0.0027% of 8B LLMs) while maintaining top-tier performance!

Elated to announce that DELIFT has been accepted to ICLR'25 🎉 Looking forward to discussing it in Singapore!

Ishika Agarwal@wonderingishika.bsky.social · 2y ago

I'm so excited to share my latest paper called DELIFT along with Krishnateja Killamsetty, Lucian Popa, and Marina Danilevksy at IBM Research 🎉 We tackle expensive fine-tuning by selecting a small subset of informative data that targets a model's weaknesses.

Congratulations to @dilekh.bsky.social for her ACL Fellowship! 🎉🎉🎉 www.aclweb.org/portal/conte...

The last response from Gemini in this thread may shock you: gemini.google.com/share/6d141b...

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Can LLMs make us critical thinkers? TreeInstruct reorients assistant-like LLMs to be instructors that guide students towards understanding their mistakes, without providing direct/indirect answers. Check out aclanthology.org/2024.finding... (w/ @wonderingishika.bsky.social) to learn more!

All around the theme of data-efficient NLP: (1) using influence functions to improve language model performance from less data (2) enabling language models to generate queries for things it doesn't know

@lastpositivist.bsky.social · 2y ago

Bluesky academics, lets get to know each other! Quote this & tell me: 1) a project you are working on & 2) an odd idea/theory you aren’t working on but keep thinking about 1. I came to hate my work and thinking so don't do it anymore. 2.

I'm so excited to share my latest paper called DELIFT along with Krishnateja Killamsetty, Lucian Popa, and Marina Danilevksy at IBM Research 🎉 We tackle expensive fine-tuning by selecting a small subset of informative data that targets a model's weaknesses.

Can LLMs make us critical thinkers? TreeInstruct reorients LLMs to be instructors that guide students socratically to solve problems, instead of assistants that provide direct answers. Check out our EMNLP2024 paper at arxiv.org/abs/2406.11709 (w/ @pkargupta.bsky.social) to learn more!

Instruct, Not Assist: LLM-based Multi-Turn Planning and Hierarchical Questioning for Socratic Code Debugging

Socratic questioning is an effective teaching strategy, encouraging critical thinking and problem-solving. The conversational capabilities of large language models (LLMs) show great potential for prov...

arxiv.org