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Original Paper: https://arxiv.org/abs/2404.03592 By: Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, Christopher Potts Abstract: Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown
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Original Paper: https://arxiv.org/pdf/2310.06825.pdf By: Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,
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Original Paper: https://arxiv.org/abs/2310.04948 By: Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu Abstract: The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications
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Original Paper: https://arxiv.org/abs/2311.01449 By: Chau Minh Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, Mohit Iyyer Abstract: Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea
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The Double-Edged Sword of AI in Hardware Design The world of tech is always moving fast, and one of the coolest advances has been in making computer hardware. But as these designs get more complex, they also get trickier to keep safe and secure. That's where Large Language
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Introduction In the world of artificial intelligence (AI), making sure AI systems align with our values and societal norms is super important. While older methods like RLHF and CAI have made some progress, they depend too much on people's input and set rules. That's where IterAlign
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"In the world of artificial intelligence, the right question is not just half the answer—it's the beginning of a journey into the depths of machine understanding." - An AI Enthusiast The Challenge at Hand In the rapidly evolving landscape of AI, Large Language Models (LLMs)
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Original Paper: https://arxiv.org/abs/2211.10438 By: Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han Abstract: Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods cannot maintain accuracy and hardware efficiency
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Original Paper: https://arxiv.org/abs/2403.19103 By: Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter Abstract: Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious
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Original Paper: https://arxiv.org/abs/2310.17569 By: Xinghui Li, Jingyi Lu, Kai Han, Victor Prisacariu Abstract: In this paper, we address the challenge of matching semantically similar keypoints across image pairs. Existing research indicates that the intermediate output of the UNet within the Stable Diffusion (SD) can serve
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Original Paper: https://arxiv.org/abs/2403.15875 By: Zhicheng Du, Zhaotian Xie, Yan Tong, Peiwu Qin Abstract: This study constructs the LanguAge Model with Prompt EngineeRing (LAMPER) framework, designed to systematically evaluate the adaptability of pre-trained language models (PLMs) in accommodating diverse prompts and their integration in zero-shot time
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Original Paper: https://arxiv.org/abs/2304.14670 By: Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang, Dajiang Zhu, Yixuan
