Guanchu Wang

🚀 Can LLMs stop overthinking when detailed reasoning isn't needed? Excited to share our latest work on LLM reasoning: AutoL2S 🧠⚡ 📄 Paper: arxiv.org/abs/2505.22662 🤖 Model: huggingface.co/amandaa/AutoL2… LLMs often overthink—generating unnecessarily long CoTs even for easy questions, increasing cost & latency. We propose Auto Long-Short Reasoning (AutoL2S): A model-agnostic framework that dynamically choose long or short reasoning based on question complexity. 💡 Just add a token—that's all it takes to teach the model when to skip redundant steps. 🖼️ (See below 👇) How AutoL2S switches reasoning strategies using simple markers like and → 📉 Up to 57% reduction in CoT length across four reasoning tasks without performance drop. Credits to all co-authors: @FengLuo895614 *, @YuNengChuang*, @Guanchu_Gary, Hoang Anh Duy Le, @henryzhongsc , Hongyi Liu, @jiayiy , @YangSui, Vladimir Braverman, Vipin Chaudhary, @huxia

@Yuchenj_UW 📢Excited to present "Taylor Unswift" poster at #EMNLP24 in Miami! Join us on Nov 13 (Wed), 10:30–12:00, at Main #778. "Taylor Unswift" aims to solve the dilemma of secured weight release for Llama. 🔗Paper: arxiv.org/pdf/2410.05331 🔗Code: github.com/guanchuwang/Ta…

After "Attention Is All You Need", AI paper titles be like:

@YuNengChuang Thank you Allen!

📢Excited to present "Taylor Unswift" poster at #EMNLP24 in Miami! Join us on Nov 13 (Wed), 10:30–12:00, at Main #778. "Taylor Unswift" aims to solve the dilemma of secured weight release for LLM developers and users. 🔗Paper: arxiv.org/pdf/2410.05331 🔗Code: github.com/guanchuwang/Ta… Wanna know more about "Taylor Unswift"😉: 🚨 Oftentimes, model developers face a dilemma: open-source their models and lose control, or offer closed APIs but bear costs and deter privacy-conscious users. 🚑 Introducing "Taylor Unswift": a method using Taylor Expansion Theory to protect model weights while allowing users to run models on their own data without accessing the weights. These correspond to the 'Taylor' and 'Unswift' in the title. 🌟 Developers can prevent misuse of their models, while users can run models on their own data without sharing it—unlike with services like the ChatGPT API. More detailed insights can be found in the paper! Kudos to all co-authors: @Guanchu_Gary*, @YuNengChuang*, @RuixiangT, @henryzhongsc, @jiayiy, @serendip410, @ziruirayliu, Vipin Chaudhary, Shuai Xu, James Caverlee, @huxia #LLM #security #NLP #EMNLP

Introducing the LTSM-bundle Package! 🌟Thrilled to launch our open-source tool 🔧Assess various crucial designs to train Large Time Series Models (LTSMs), and identity the best training practices 🔗 Paper: arxiv.org/abs/2406.14045 🔗 GitHub: github.com/daochenzha/ltsm