Summary of Pre-trained Large Language Models Use Fourier Features to Compute Addition, by Tianyi Zhou et al.
Pre-trained Large Language Models Use Fourier Features to Compute Additionby Tianyi Zhou, Deqing Fu, Vatsal…
Summary of Block Transformer: Global-to-local Language Modeling For Fast Inference, by Namgyu Ho et al.
Block Transformer: Global-to-Local Language Modeling for Fast Inferenceby Namgyu Ho, Sangmin Bae, Taehyeon Kim, Hyunjik…
Summary of Aligning Large Language Models Via Fine-grained Supervision, by Dehong Xu et al.
Aligning Large Language Models via Fine-grained Supervisionby Dehong Xu, Liang Qiu, Minseok Kim, Faisal Ladhak,…
Summary of Contextual Counting: a Mechanistic Study Of Transformers on a Quantitative Task, by Siavash Golkar et al.
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Taskby Siavash Golkar, Alberto Bietti,…
Summary of Loki: Low-rank Keys For Efficient Sparse Attention, by Prajwal Singhania et al.
Loki: Low-rank Keys for Efficient Sparse Attentionby Prajwal Singhania, Siddharth Singh, Shwai He, Soheil Feizi,…
Summary of Parrot: Multilingual Visual Instruction Tuning, by Hai-long Sun et al.
Parrot: Multilingual Visual Instruction Tuningby Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi,…
Summary of Ffnet: Metamixer-based Efficient Convolutional Mixer Design, by Seokju Yun et al.
FFNet: MetaMixer-based Efficient Convolutional Mixer Designby Seokju Yun, Dongheon Lee, Youngmin RoFirst submitted to arxiv…
Summary of Decomposing and Interpreting Image Representations Via Text in Vits Beyond Clip, by Sriram Balasubramanian et al.
Decomposing and Interpreting Image Representations via Text in ViTs Beyond CLIPby Sriram Balasubramanian, Samyadeep Basu,…
Summary of Timecma: Towards Llm-empowered Multivariate Time Series Forecasting Via Cross-modality Alignment, by Chenxi Liu et al.
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentby Chenxi Liu, Qianxiong Xu, Hao…
Summary of Understanding Token Probability Encoding in Output Embeddings, by Hakaze Cho et al.
Understanding Token Probability Encoding in Output Embeddingsby Hakaze Cho, Yoshihiro Sakai, Kenshiro Tanaka, Mariko Kato,…