Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds | Paper Citation Record

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This paper cites Transformers learn to implement preconditioned gradient descent for in-context learning.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Transformers learn to implement preconditioned gradient descent for in-context learning

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Unresolved cited work

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This paper cites Language models are few-shot learners.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Language models are few-shot learners

Reference 4

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This paper cites Reading Wikipedia to Answer Open-Domain Questions.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Reading Wikipedia to Answer Open-Domain Questions

Reference 5

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This paper cites Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

Reference 6

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This paper cites Exploring the robustness of in-context learning with noisy labels.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Exploring the robustness of in-context learning with noisy labels

Reference 7

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This paper cites UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation

Reference 8

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Nearest neighbor pattern classification

Reference 9

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This paper cites A Survey on In-context Learning.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds A Survey on In-context Learning

Reference 10

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This paper cites A survey on in-context learning.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds A survey on in-context learning

Reference 11

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

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds What can transformers learn in-context? a case study of simple function classes

Reference 12

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This paper cites Test-time training provably improves transformers as in-context learners.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Test-time training provably improves transformers as in-context learners

Reference 13

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This paper cites RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models

Reference 14

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This paper cites Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models

Reference 15

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This paper cites On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables

Reference 16

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This paper cites Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering

Reference 17

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This paper cites Atlas: Few-shot Learning with Retrieval Augmented Language Models.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Atlas: Few-shot Learning with Retrieval Augmented Language Models

Reference 18

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This paper cites T rivia QA : A large scale distantly supervised challenge dataset for reading comprehension.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds T rivia QA : A large scale distantly supervised challenge dataset for reading comprehension

Reference 19

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This paper cites Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 20

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models

Reference 21

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds More documents, same length: Isolating the challenge of multiple documents in rag

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\

Reference 23

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This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 24

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Making large language models a better foundation for dense retrieval

Reference 25

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Self-Prompting Large Language Models for Zero-Shot Open-Domain QA

Reference 26

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Mot: Pre-thinking and recalling enable chatgpt to self-improve with memory-of-thoughts

Reference 27

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Chain-of-knowledge: Grounding large language models via dynamic knowledge adapting over heterogeneous sources

Reference 28

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds What Makes Good In-Context Examples for GPT-$3$?

Reference 29

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds In-context Learning with Retrieved Demonstrations for Language Models: A Survey

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Sfr-embedding-mistral:enhance text retrieval with transfer learning

Reference 32

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds MetaICL: Learning to Learn In Context

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds The matrix cookbook

Reference 34

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds In-context retrieval-augmented language models

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Smallcap: Lightweight image captioning prompted with retrieval augmentation

Reference 36

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Retrieval-augmented transformer for image captioning

Reference 37

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

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This paper cites Can In-context Learning Really Generalize to Out-of-distribution Tasks?.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Can In-context Learning Really Generalize to Out-of-distribution Tasks?

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Certifiably robust rag against retrieval corruption

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 42

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds RECOMP : Improving retrieval-augmented LM s with context compression and selective augmentation

Reference 43

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Is Retriever Merely an Approximator of Reader?

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Compositional exemplars for in-context learning

Reference 45

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This paper cites Making Retrieval-Augmented Language Models Robust to Irrelevant Context.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Making Retrieval-Augmented Language Models Robust to Irrelevant Context

Reference 46

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Trained transformers learn linear models in-context

Reference 47

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Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Automatic Chain of Thought Prompting in Large Language Models

Reference 48

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This paper cites Retrieval-Augmented Generation for AI-Generated Content: A Survey.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds Retrieval-Augmented Generation for AI-Generated Content: A Survey

Reference 49

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This paper cites NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning.

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds NoisyICL: A Little Noise in Model Parameters Calibrates In-context Learning

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