Selective Attention: Enhancing Transformer through Principled Context Control | Paper Citation Record

Observation e4c03ae0-6ca2-43c9-8bfd-061929b7d7aa · outbound

This paper cites Generalization on the unseen, logic reasoning and degree curriculum.

Selective Attention: Enhancing Transformer through Principled Context Control Generalization on the unseen, logic reasoning and degree curriculum

Reference 1

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Observation d16b9858-1456-49ef-bd25-cb7b6524ae0e · outbound

This paper cites Simple linear attention language models balance the recall-throughput tradeoff.

Selective Attention: Enhancing Transformer through Principled Context Control Simple linear attention language models balance the recall-throughput tradeoff

Reference 2

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Observation 7e584632-0adf-459b-bcd1-a74b27936164 · outbound

This paper cites Pythia: A suite for analyzing large language models across training and scaling.

Selective Attention: Enhancing Transformer through Principled Context Control Pythia: A suite for analyzing large language models across training and scaling

Reference 3

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Observation 4e0e51ba-d25e-4ac4-bd08-b1e4c0b4a770 · outbound

This paper cites Piqa: Reasoning about phys- ical commonsense in natural language.

Selective Attention: Enhancing Transformer through Principled Context Control Piqa: Reasoning about phys- ical commonsense in natural language

Reference 4

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Observation 134d0f6c-7f9b-4447-867b-55b3c34f0d33 · outbound

This paper cites Language models are few-shot learners.

Selective Attention: Enhancing Transformer through Principled Context Control Language models are few-shot learners

Reference 5

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Observation 465b0d33-9678-4e9e-81d6-1ba9e8434219 · outbound

This paper cites Scatterbrain: Unifying sparse and low-rank attention.

Selective Attention: Enhancing Transformer through Principled Context Control Scatterbrain: Unifying sparse and low-rank attention

Reference 6

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Observation b29b67e7-fe97-4854-ac37-8fb24f3c14bf · outbound

This paper cites Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation.

Selective Attention: Enhancing Transformer through Principled Context Control Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation

Reference 7

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Observation a4f4835b-7aa1-4246-a06d-93fbee0e238a · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Selective Attention: Enhancing Transformer through Principled Context Control Generating Long Sequences with Sparse Transformers

Reference 8

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Observation 7babb13d-2381-4da3-9191-0bfd022f5961 · outbound

This paper cites Palm: Scaling language modeling with pathways.

Selective Attention: Enhancing Transformer through Principled Context Control Palm: Scaling language modeling with pathways

Reference 9

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Observation 2a7830f5-5812-4c78-996c-f04f888ab748 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Selective Attention: Enhancing Transformer through Principled Context Control Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 10

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Observation 01245eda-53d6-4d45-ae9e-a5fdfce0243e · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Selective Attention: Enhancing Transformer through Principled Context Control FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 11

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Observation 2cf976a9-74fd-43f0-918e-464c5ab7b23d · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.

Selective Attention: Enhancing Transformer through Principled Context Control Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 12

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Observation 0a8f6f18-0558-4bd9-8e71-3efd00f61b86 · outbound

This paper cites Language modeling with gated convolutional networks.

Selective Attention: Enhancing Transformer through Principled Context Control Language modeling with gated convolutional networks

Reference 13

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Observation 4ccf5561-4705-4e92-ba3c-7ceb6021ba38 · outbound

This paper cites Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models.

Selective Attention: Enhancing Transformer through Principled Context Control Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Reference 14

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Observation 6bc3b1c0-9a81-4433-a9e3-bdb308a8a9ea · outbound

This paper cites Attention is not all you need: Pure attention loses rank doubly exponentially with depth.

Selective Attention: Enhancing Transformer through Principled Context Control Attention is not all you need: Pure attention loses rank doubly exponentially with depth

Reference 15

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Observation 0f4c0856-bb0e-4463-bb79-8a24fc5685e7 · outbound

This paper cites The Llama 3 Herd of Models.

Selective Attention: Enhancing Transformer through Principled Context Control The Llama 3 Herd of Models

Reference 16

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Observation a95a4e96-1b86-4910-ad5e-675d7a23820b · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

Selective Attention: Enhancing Transformer through Principled Context Control The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 17

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Observation 6b456d1e-30ec-4684-90d6-45fbc373eb66 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Selective Attention: Enhancing Transformer through Principled Context Control Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 18

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Observation 8b8be8c8-6bad-486b-b6df-16ee79492774 · outbound

This paper cites Long short-term memory.

Selective Attention: Enhancing Transformer through Principled Context Control Long short-term memory

Reference 19

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Observation bb862eb3-8d67-4908-8b40-fedb66f1561a · outbound

This paper cites From self-attention to markov models: Unveiling the dynamics of generative transformers.

Selective Attention: Enhancing Transformer through Principled Context Control From self-attention to markov models: Unveiling the dynamics of generative transformers

Reference 20

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Observation 11150208-117f-4ebd-be29-2eb91f3af28d · outbound

This paper cites GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling.

Selective Attention: Enhancing Transformer through Principled Context Control GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling

Reference 21

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Observation 3e1023c4-1e10-4820-bb86-e58ff4f4a18d · outbound

This paper cites Au- tobalance: Optimized loss functions for imbalanced data.

Selective Attention: Enhancing Transformer through Principled Context Control Au- tobalance: Optimized loss functions for imbalanced data

Reference 22

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Observation 84955bea-7ea9-4a27-b52f-3c9bea82cf3f · outbound

This paper cites Exposing attention glitches with flip-flop language modeling.

Selective Attention: Enhancing Transformer through Principled Context Control Exposing attention glitches with flip-flop language modeling

Reference 23

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Observation 12495330-3e98-4525-bd5d-28429bf2b89a · outbound

This paper cites Decoupled Weight Decay Regularization.

Selective Attention: Enhancing Transformer through Principled Context Control Decoupled Weight Decay Regularization

Reference 24

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Observation 755132b2-c44f-48e2-8d58-0c342a3ec52f · outbound

This paper cites Long range language modeling via gated state spaces.

Selective Attention: Enhancing Transformer through Principled Context Control Long range language modeling via gated state spaces

Reference 25

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Observation 8af3079e-e0cc-4aa7-b1cf-c95e3b1e17cf · outbound

This paper cites Long-tail learning via logit adjustment.

Selective Attention: Enhancing Transformer through Principled Context Control Long-tail learning via logit adjustment

Reference 26

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Observation 01d99d14-a619-4f81-9054-122e6971f143 · outbound

This paper cites Pointer Sentinel Mixture Models.

Selective Attention: Enhancing Transformer through Principled Context Control Pointer Sentinel Mixture Models

Reference 27

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Observation 21757244-e032-4d60-bed0-82fe1f80280e · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Selective Attention: Enhancing Transformer through Principled Context Control Efficient Estimation of Word Representations in Vector Space

Reference 28

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Observation c36da27f-e4d3-43f7-87df-58dde1c8838f · outbound

This paper cites Landmark Attention: Random-Access Infinite Context Length for Transformers.

Selective Attention: Enhancing Transformer through Principled Context Control Landmark Attention: Random-Access Infinite Context Length for Transformers

Reference 29

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Observation fe810b72-7f78-4bff-8447-3fd4af5875e2 · outbound

This paper cites In-context Learning and Induction Heads.

Selective Attention: Enhancing Transformer through Principled Context Control In-context Learning and Induction Heads

Reference 30

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Observation 80e00af2-9827-4e86-95bc-74c10e5c399e · outbound

This paper cites Fast attention over long sequences with dynamic sparse flash attention.

Selective Attention: Enhancing Transformer through Principled Context Control Fast attention over long sequences with dynamic sparse flash attention

Reference 31

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Observation bb298cb2-55eb-4355-93be-8d9e2befbdb9 · outbound

This paper cites The LAMBADA dataset: Word prediction requiring a broad discourse context.

Selective Attention: Enhancing Transformer through Principled Context Control The LAMBADA dataset: Word prediction requiring a broad discourse context

Reference 32

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Observation a2cf47ee-e863-4c1d-a6c3-da4fb6456a47 · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Selective Attention: Enhancing Transformer through Principled Context Control YaRN: Efficient Context Window Extension of Large Language Models

Reference 33

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Observation 7c6e2e70-e19c-44f9-8b7d-b5bc80adeefa · outbound

This paper cites Language models are unsupervised multitask learners.

Selective Attention: Enhancing Transformer through Principled Context Control Language models are unsupervised multitask learners

Reference 34

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Observation 8eb263be-f9bf-4a60-8f80-1a5e48c70134 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Selective Attention: Enhancing Transformer through Principled Context Control Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 35

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Observation 4b4ea709-7189-4bf2-b357-90b05c696a41 · outbound

This paper cites Sparse modular activation for efficient sequence modeling.

Selective Attention: Enhancing Transformer through Principled Context Control Sparse modular activation for efficient sequence modeling

Reference 36

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Observation 1339a8e0-be48-4ec3-a062-c9bad9871058 · outbound

This paper cites Unraveling attention via convex duality: Analysis and interpretations of vision transformers.

Selective Attention: Enhancing Transformer through Principled Context Control Unraveling attention via convex duality: Analysis and interpretations of vision transformers

Reference 37

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Observation ac92af6a-575e-4e2e-90d6-c47f2a62a74f · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Selective Attention: Enhancing Transformer through Principled Context Control Winogrande: An adversarial winograd schema challenge at scale

Reference 38

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Observation a7ee327f-6ecd-42a9-a81f-370b9b71131f · outbound

This paper cites Multitask Prompted Training Enables Zero-Shot Task Generalization.

Selective Attention: Enhancing Transformer through Principled Context Control Multitask Prompted Training Enables Zero-Shot Task Generalization

Reference 39

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Observation 1a99ab6c-ec1d-4e8f-881c-36bf2aae7cdb · outbound

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Selective Attention: Enhancing Transformer through Principled Context Control GLU Variants Improve Transformer

Reference 40

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Observation ebe21000-6a5f-45b6-bbfd-3dd12ffc181d · outbound

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Selective Attention: Enhancing Transformer through Principled Context Control SlimPajama-DC: Understanding Data Combinations for LLM Training

Reference 41

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This paper cites Roformer: Enhanced transformer with rotary position embedding.

Selective Attention: Enhancing Transformer through Principled Context Control Roformer: Enhanced transformer with rotary position embedding

Reference 42

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Observation dc4a55a4-ae75-4bcf-8855-8eacb7bd399e · outbound

This paper cites Max-margin token selection in attention mechanism.

Selective Attention: Enhancing Transformer through Principled Context Control Max-margin token selection in attention mechanism

Reference 43

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Observation ced6b819-a208-4a8e-957f-09d6982126fd · outbound

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Selective Attention: Enhancing Transformer through Principled Context Control LLaMA: Open and Efficient Foundation Language Models

Reference 44

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Observation e41068ad-ec21-4aae-bc13-10b02c123904 · outbound

This paper cites Attention is all you need.

Selective Attention: Enhancing Transformer through Principled Context Control Attention is all you need

Reference 45

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Observation 7d7085b9-10ea-4a38-903b-55c162e0635b · outbound

This paper cites MambaByte: Token-free Selective State Space Model.

Selective Attention: Enhancing Transformer through Principled Context Control MambaByte: Token-free Selective State Space Model

Reference 46

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Observation d199088d-4baa-42e3-8fd6-9d7e7d00faa0 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

Selective Attention: Enhancing Transformer through Principled Context Control An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 47

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This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Selective Attention: Enhancing Transformer through Principled Context Control Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 48

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Observation dd23a109-9351-4129-bc16-20ae39c55522 · outbound

This paper cites Self-attention networks can process bounded hierarchical languages.

Selective Attention: Enhancing Transformer through Principled Context Control Self-attention networks can process bounded hierarchical languages

Reference 49

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Observation 072d5fac-31bd-43e9-8efa-494a3927a17b · outbound

This paper cites Differential Transformer.

Selective Attention: Enhancing Transformer through Principled Context Control Differential Transformer

Reference 50

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Observation 066b80dd-0efd-44ee-8478-ff41540eb413 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Selective Attention: Enhancing Transformer through Principled Context Control HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 51

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Observation a1b27eb4-7a7c-4d55-8e76-3c194670fcde · outbound

This paper cites Class- attribute priors: Adapting optimization to heterogeneity and fairness objective.

Selective Attention: Enhancing Transformer through Principled Context Control Class- attribute priors: Adapting optimization to heterogeneity and fairness objective

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:12:33.282230Z

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Observation 10b48b63-1475-44e8-ab0d-99c91d95bbcf · outbound

This paper cites What Algorithms can Transformers Learn? A Study in Length Generalization.

Selective Attention: Enhancing Transformer through Principled Context Control What Algorithms can Transformers Learn? A Study in Length Generalization

Reference 53

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Observation a5a1cb71-073a-400c-a639-2688cb580694 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

Selective Attention: Enhancing Transformer through Principled Context Control Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 54

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