Top Tweets for #InfluenceFunctions
π Tired of slow, expensive data selection for training LLMs?
Still using #ShapleyValues or #InfluenceFunctions to find high-quality data, and burning through compute? π©π₯πΈ
π The new @iclr_conf work of @xiaoqiang_98
@michael_xinyi on Efficient Top-m Data Values Identification for #DataSelection might just be the time/compute-saver you've been waiting for!
We present a scalable and principled data selection algorithm that accelerates any value-based method, with guaranteed correctness and provable complexity! ππ§ β
π‘ Hereβs what you get:
β‘ 50Γ speedup over existing baselines
π― 21.7% of the compute needed to select a 5% subset that outperforms the full dataset for LLM data selection
π 36.6% of the compute needed to match the performance of influence function methods for LLM data selection
π Theoretical guarantee: We rigorously analyze both the correctness and complexity of our algorithm in the paper
π Whether you're scaling #LLMs or optimizing your data, our algorithm delivers smarter and faster data selection, and considerable compute savings β all backed by theory.
Check out our #ICLR2025 Poster #618 in Poster Session 1 Thurs 10am β we're redefining what's possible in #LLM data selection! πβ¨
Paper: https://t.co/t815YM7429
GitHub: https://t.co/RFAFOu6zCz
@NicolaFitz @hines8 @podTockom @nshejazi @dscharf3 @TheEuroCIM Nice choice of pics: pretty plots! We want to contribute this one to your collection #causalinference with #influencefunctions from our keynote speaker @edwardhkennedy

Super talks this afternoon on recent research in #causalinference with #influencefunctions by @hines8 @podTockom @nshejazi @dscharf3 @TheEuroCIM hosted by @LSHTMstatmethod

@botnet_hunter @cylanceinc Thanks for the suggestion! We read the paper and liked it so much that we posted a summary on our blog. Feel free to check it out, would appreciate any feedback! #InfluenceFunctions #DeepLearningThesis https://t.co/MPtckus2tN
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