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The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
Every paper prepared on Reading + AI, newest first.
Efficiently Modeling Long Sequences with Structured State Spaces
Albert Gu, Karan Goel, Christopher Ré
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they…
Lost in the Middle: How Language Models Use Long Contexts
Nelson F. Liu Kevin Lin John Hewitt Ashwin ParanjapeMichele Bevilacqua Fabio Petroni Percy LiangStanford University University of California, Berkeley Samaya AI
While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-va…
Ruler: What’s the Real Context Size of Your Long-Context Language Models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman et al.
The needle-in-a-haystack (NIAH) test, which examines the ability to retrieve a piece of information (the “needle”) from long distractor texts (the “haystack”), has been widely adopted to evaluate long-context language models (LMs). However, this simple retrieval-based test is indicative of only a superficial form of l…
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Y. X. Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng
Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention offers a promising direction for improving efficiency while maintaining model capabilities. We present NSA, a Natively trai…
Gated Delta Networks: Improving Mamba2 with Delta Rule
Songlin Yang, Jan Kautz, Ali Hatamizadeh
Linear Transformers have gained attention as efficient alternatives to standard Transformers, but their performance in retrieval and long-context tasks has been limited. To address these limitations, recent work has explored two distinct mechanisms: gating for adaptive memory control and the delta update rule for prec…
Efficient Streaming Language Models with Attention Sinks
Guangxuan Xiao, Yuandong Tian, Beidi Chen et al.
Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges. Firstly, during the decoding stage, caching previous tokens’ Key and Value states (KV) consumes extensive memory. Secondly, popular LLM…
YaRN: Efficient Context Window Extension of Large Language Models
Bowen Peng, Jeffrey Quesnelle, Honglu Fan et al.
Rotary Position Embeddings (RoPE) have been shown to effectively encode positional information in transformer-based language models. However, these models fail to generalize past the sequence length they were trained on. We present YaRN (Yet another RoPE extensioN method), a compute-efficient method to extend the cont…
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas et al.
Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input’s length, they are prohibitively slow for very long sequences. To address this limitation, we express the self-attention as a linear dot-product of kernel feature maps and make use of the assoc…
Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
Ofir Press, Noah A. Smith, Mike Lewis
Since the introduction of the transformer model by [Vaswani et al. (2017)](#bib.bib39), a fundamental question has yet to be answered: how does a model achieve extrapolation at inference time for sequences that are longer than it saw during training? We first show that extrapolation can be enabled by simply changing t…
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Albert Gu, Tri Dao
Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated convolution and recurrent models, and structured state space models (SSMs)…
RWKV: Reinventing RNNs for the Transformer Era
Bo Peng Eric Alcaide Quentin Anthony, Alon Albalak Samuel Arcadinho Stella Biderman Huanqi Cao Xin Cheng, Michael Chung Xingjian Du Matteo Grella Kranthi Kiran GV Xuzheng He et al.
Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the…
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Tri Dao, Albert Gu
While Transformers have been the main architecture behind deep learning’s success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale. We show that these families of models are actually quite closely related, and develop a…