Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning GPT-4 Technical Report

Reference 1

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This paper cites What learning algorithm is in-context learning? investigations with linear models.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning What learning algorithm is in-context learning? investigations with linear models

Reference 2

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This paper cites Transformers as statisticians: Provable in-context learning with in-context algorithm selection.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Transformers as statisticians: Provable in-context learning with in-context algorithm selection

Reference 3

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This paper cites On the Opportunities and Risks of Foundation Models.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning On the Opportunities and Risks of Foundation Models

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This paper cites Dick, Hidenori Tanaka, and Tomer Ullman.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Dick, Hidenori Tanaka, and Tomer Ullman

Reference 5

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This paper cites Rademacher and gaussian complexities: Risk bounds and structural results.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Rademacher and gaussian complexities: Risk bounds and structural results

Reference 6

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This paper cites Language models are few-shot learners.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Language models are few-shot learners

Reference 7

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This paper cites Palm: Scaling language modeling with pathways.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Palm: Scaling language modeling with pathways

Reference 8

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 9

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This paper cites A survey on in-context learning.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning A survey on in-context learning

Reference 10

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This paper cites In-context learning and gradient descent revisited.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning In-context learning and gradient descent revisited

Reference 11

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This paper cites Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Why can gpt learn in-context? language models secretly perform gradient descent as meta-optimizers

Reference 12

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This paper cites A mathematical framework for transformer circuits.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning A mathematical framework for transformer circuits

Reference 13

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This paper cites The evolution of statistical induction heads: In-context learning markov chains.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning The evolution of statistical induction heads: In-context learning markov chains

Reference 14

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This paper cites Transformers learn to achieve second-order convergence rates for in-context linear regression.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Transformers learn to achieve second-order convergence rates for in-context linear regression

Reference 15

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This paper cites How do transformers learn in-context beyond simple functions? a case study on learning with representations.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning How do transformers learn in-context beyond simple functions? a case study on learning with representations

Reference 16

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This paper cites What can transformers learn in-context? a case study of simple function classes.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning What can transformers learn in-context? a case study of simple function classes

Reference 17

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This paper cites A Theory of Emergent In-Context Learning as Implicit Structure Induction.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning A Theory of Emergent In-Context Learning as Implicit Structure Induction

Reference 18

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This paper cites Distilling the Knowledge in a Neural Network.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Distilling the Knowledge in a Neural Network

Reference 19

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Observation a02e7793-c9c2-42b4-a639-3d29d675903a · outbound

This paper cites Lee, Qi Lei, and Benjamin Van Roy.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Lee, Qi Lei, and Benjamin Van Roy

Reference 20

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This paper cites Decoupled weight decay regularization.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Decoupled weight decay regularization

Reference 21

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This paper cites Transformers as algorithms: Generalization and stability in in-context learning.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Transformers as algorithms: Generalization and stability in in-context learning

Reference 22

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This paper cites The closeness of in-context learning and weight shifting for softmax regression.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning The closeness of in-context learning and weight shifting for softmax regression

Reference 23

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This paper cites Probability in Banach Spaces: isoperimetry and processes.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Probability in Banach Spaces: isoperimetry and processes

Reference 24

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This paper cites Effects of Prompt Length on Domain-specific Tasks for Large Language Models.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Effects of Prompt Length on Domain-specific Tasks for Large Language Models

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 26

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This paper cites On the method of bounded differences.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning On the method of bounded differences

Reference 27

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Unresolved cited work

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Unresolved cited work

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This paper cites Metaicl: Learning to learn in context.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Metaicl: Learning to learn in context

Reference 30

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning In-context Learning and Induction Heads

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning In-context learning through the bayesian prism

Reference 32

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Improving systematic generalization of linear transformer using normalization layers and orthogonality loss function

Reference 33

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This paper cites Fitnets: Hints for thin deep nets.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Fitnets: Hints for thin deep nets

Reference 34

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Towards understanding how transformers learn in-context through a representation learning lens

Reference 35

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Prompt programming for large language models: Beyond the few-shot paradigm

Reference 36

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This paper cites Improving language understanding by generative pre-training.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Improving language understanding by generative pre-training

Reference 37

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 38

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Language models are unsupervised multitask learners

Reference 39

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This paper cites Schema-learning and rebinding as mechanisms of in-context learning and emergence.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Schema-learning and rebinding as mechanisms of in-context learning and emergence

Reference 40

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This paper cites Position: Do pretrained transformers learn in-context by gradient descent? In Forty-first International Conference on Machine Learning , 2024.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Position: Do pretrained transformers learn in-context by gradient descent? In Forty-first International Conference on Machine Learning , 2024

Reference 41

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning LLaMA: Open and Efficient Foundation Language Models

Reference 42

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Function vectors in large language models

Reference 43

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This paper cites Transformers learn in-context by gradient descent.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Transformers learn in-context by gradient descent

Reference 44

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This paper cites Uncovering mesa-optimization algorithms in Transformers.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Uncovering mesa-optimization algorithms in Transformers

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Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Attention is all you need

Reference 46

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This paper cites The learnability of in-context learning.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning The learnability of in-context learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:19.071328Z

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This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Chain-of-thought prompting elicits reasoning in large language models

Reference 48

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This paper cites Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Large language models are implicitly topic models: Explaining and finding good demonstrations for in-context learning

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:19.043900Z

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This paper cites An explanation of in-context learning as implicit bayesian inference.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning An explanation of in-context learning as implicit bayesian inference

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:19.025769Z

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This paper cites Can we edit factual knowledge by in-context learning? In The 2023 Conference on Empirical Methods in Natural Language Processing , 2023.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Can we edit factual knowledge by in-context learning? In The 2023 Conference on Empirical Methods in Natural Language Processing , 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:19.010308Z

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This paper cites The mystery of in-context learning: A comprehensive survey on interpretation and analysis.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning The mystery of in-context learning: A comprehensive survey on interpretation and analysis

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:18.994479Z

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This paper cites Root mean square layer normalization.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning Root mean square layer normalization

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:18.978692Z

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This paper cites What and how does in-context learning learn? bayesian model averaging, parameterization, and generalization.

Brewing Knowledge in Context: Distillation Perspectives on In-Context Learning What and how does in-context learning learn? bayesian model averaging, parameterization, and generalization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:11:18.963408Z

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