Exploring Imbalanced Annotations for Effective In-Context Learning | Paper Citation Record

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This paper cites Language models are few-shot learners.

Exploring Imbalanced Annotations for Effective In-Context Learning Language models are few-shot learners

Reference 1

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Observation 01f320fd-2ee2-4426-ac9d-577f56dc3943 · outbound

This paper cites Few- shot fine-tuning vs.

Exploring Imbalanced Annotations for Effective In-Context Learning Few- shot fine-tuning vs

Reference 2

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Observation 7f771c63-140e-4690-9343-95b6caf18f24 · outbound

This paper cites In-context learning through the bayesian prism.

Exploring Imbalanced Annotations for Effective In-Context Learning In-context learning through the bayesian prism

Reference 3

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Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 4

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Observation 07dec11a-008f-4237-8c6c-3a74688b250d · outbound

This paper cites What makes multimodal in-context learning work? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1539–1550, 2024.

Exploring Imbalanced Annotations for Effective In-Context Learning What makes multimodal in-context learning work? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1539–1550, 2024

Reference 5

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Observation 12ba28c3-b65c-4ed4-8137-b5c64d040302 · outbound

This paper cites Bayesian example selection improves in-context learning for speech, text and visual modalities.

Exploring Imbalanced Annotations for Effective In-Context Learning Bayesian example selection improves in-context learning for speech, text and visual modalities

Reference 6

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Observation f2cf91d4-0c1d-4d5e-9c61-f83fec25700a · outbound

This paper cites Open-sampling: Exploring out-of-distribution data for re-balancing long-tailed datasets.

Exploring Imbalanced Annotations for Effective In-Context Learning Open-sampling: Exploring out-of-distribution data for re-balancing long-tailed datasets

Reference 7

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Observation 60546d13-64ae-4fae-bb8d-127c91d0fa11 · outbound

This paper cites A survey of deep long-tail classification advance- ments.

Exploring Imbalanced Annotations for Effective In-Context Learning A survey of deep long-tail classification advance- ments

Reference 8

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This paper cites Class-balanced loss based on effective number of samples.

Exploring Imbalanced Annotations for Effective In-Context Learning Class-balanced loss based on effective number of samples

Reference 9

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Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 10

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Observation b5f30352-02cd-4390-b33a-abf9c438a32f · outbound

This paper cites Character-level convolutional networks for text classification.

Exploring Imbalanced Annotations for Effective In-Context Learning Character-level convolutional networks for text classification

Reference 11

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Observation ba5b19ec-bb68-4dbf-8aa4-46fdd279102a · outbound

This paper cites CARER: Contextualized affect representations for emotion recognition.

Exploring Imbalanced Annotations for Effective In-Context Learning CARER: Contextualized affect representations for emotion recognition

Reference 12

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Observation 8026d722-6767-491b-a675-9c738a3a9e4d · outbound

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

Exploring Imbalanced Annotations for Effective In-Context Learning Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov

Reference 13

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Observation 42837baa-83a0-4852-bd7c-a0c1333cb53a · outbound

This paper cites Evaluating the state of semantic code search (2019).

Exploring Imbalanced Annotations for Effective In-Context Learning Evaluating the state of semantic code search (2019)

Reference 14

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This paper cites Revisiting demonstration selection strategies in in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Revisiting demonstration selection strategies in in-context learning

Reference 15

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Observation 37bcb061-406d-4212-b849-b6d594e015bd · outbound

This paper cites Learning to retrieve prompts for in- context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Learning to retrieve prompts for in- context learning

Reference 16

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This paper cites Compositional ex- emplars for in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Compositional ex- emplars for in-context learning

Reference 17

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Observation cd627b17-9e2f-41f8-ad65-7b16f8a7519a · outbound

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

Exploring Imbalanced Annotations for Effective In-Context Learning An explanation of in-context learning as implicit bayesian inference

Reference 18

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This paper cites OPT: Open Pre-trained Transformer Language Models.

Exploring Imbalanced Annotations for Effective In-Context Learning OPT: Open Pre-trained Transformer Language Models

Reference 19

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Observation 2965a795-dfa0-4d0d-8082-54591570f173 · outbound

This paper cites Llama 3 model card, 2024.

Exploring Imbalanced Annotations for Effective In-Context Learning Llama 3 model card, 2024

Reference 20

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This paper cites GPT-4 Technical Report.

Exploring Imbalanced Annotations for Effective In-Context Learning GPT-4 Technical Report

Reference 21

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Observation 7c244b8f-5711-4a2a-a1a6-5619ba254b93 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Exploring Imbalanced Annotations for Effective In-Context Learning Gemini: A Family of Highly Capable Multimodal Models

Reference 22

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Observation f73ddcf5-18dc-4074-9217-a1c892bd4080 · outbound

This paper cites Complementary explanations for effective in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Complementary explanations for effective in-context learning

Reference 23

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Observation cad7ef0a-581b-4bbf-a0ab-9d3024ffa5a6 · outbound

This paper cites How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669–75687, 2023.

Exploring Imbalanced Annotations for Effective In-Context Learning How re-sampling helps for long-tail learning? In Advances in Neural Information Processing Systems, pages 75669–75687, 2023

Reference 24

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Observation 8781d03d-bc8d-44c1-a9bd-563164fb723b · outbound

This paper cites Smote: synthetic minority over-sampling technique.

Exploring Imbalanced Annotations for Effective In-Context Learning Smote: synthetic minority over-sampling technique

Reference 25

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This paper cites Exploratory undersampling for class-imbalance learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Exploratory undersampling for class-imbalance learning

Reference 26

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Observation c13b1293-719c-422f-9c4e-541166529b51 · outbound

This paper cites Experiments with svm and stratified sampling with an imbalanced problem: Detection of intestinal contractions.

Exploring Imbalanced Annotations for Effective In-Context Learning Experiments with svm and stratified sampling with an imbalanced problem: Detection of intestinal contractions

Reference 27

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Observation a0925e09-03a0-4332-85ad-eb106577bb19 · outbound

This paper cites Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective.

Exploring Imbalanced Annotations for Effective In-Context Learning Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective

Reference 28

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Observation baef52be-ca54-40b5-ae41-e0e3f5369901 · outbound

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

Exploring Imbalanced Annotations for Effective In-Context Learning BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 29

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Observation fbb559a3-4f42-406f-a4ab-3828350337d9 · outbound

This paper cites MetaICL: Learning to learn in context.

Exploring Imbalanced Annotations for Effective In-Context Learning MetaICL: Learning to learn in context

Reference 30

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This paper cites Smith, and Tao Yu.

Exploring Imbalanced Annotations for Effective In-Context Learning Smith, and Tao Yu

Reference 31

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Observation ba0ec881-4f50-4253-b84c-04e002eaac1b · outbound

This paper cites Decoupling representation and classifier for long-tailed recognition.

Exploring Imbalanced Annotations for Effective In-Context Learning Decoupling representation and classifier for long-tailed recognition

Reference 32

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Observation 28e004af-3fca-4a1b-ba6e-e24fc6ecc67c · outbound

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

Exploring Imbalanced Annotations for Effective In-Context Learning What learning algorithm is in-context learning? investigations with linear models

Reference 33

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This paper cites In-context learning creates task vectors.

Exploring Imbalanced Annotations for Effective In-Context Learning In-context learning creates task vectors

Reference 34

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Observation 67b88ccc-8ac9-41ea-b248-a79324e1bb6d · outbound

This paper cites Many-Shot In-Context Learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Many-Shot In-Context Learning

Reference 35

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This paper cites A survey on in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning A survey on in-context learning

Reference 36

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Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 37

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Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 38

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Observation e5ea05df-32e5-4d28-a8b0-a86971ff684c · outbound

This paper cites Demystifying prompts in language models via perplexity estimation.

Exploring Imbalanced Annotations for Effective In-Context Learning Demystifying prompts in language models via perplexity estimation

Reference 39

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This paper cites Bayes' Power for Explaining In-Context Learning Generalizations.

Exploring Imbalanced Annotations for Effective In-Context Learning Bayes' Power for Explaining In-Context Learning Generalizations

Reference 40

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This paper cites Mitigating label biases for in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Mitigating label biases for in-context learning

Reference 41

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This paper cites On the noise robustness of in-context learning for text generation.

Exploring Imbalanced Annotations for Effective In-Context Learning On the noise robustness of in-context learning for text generation

Reference 42

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Observation 44cb079a-563c-4464-bee4-d010bef51399 · outbound

This paper cites ICL Markup: Structuring In-Context Learning using Soft-Token Tags.

Exploring Imbalanced Annotations for Effective In-Context Learning ICL Markup: Structuring In-Context Learning using Soft-Token Tags

Reference 43

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This paper cites Enhancing in-context learning via linear probe calibration.

Exploring Imbalanced Annotations for Effective In-Context Learning Enhancing in-context learning via linear probe calibration

Reference 44

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This paper cites Active example selection for in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Active example selection for in-context learning

Reference 45

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This paper cites In-context Example Selection with Influences.

Exploring Imbalanced Annotations for Effective In-Context Learning In-context Example Selection with Influences

Reference 46

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Observation 75dee881-a70e-499a-ab1b-1cf21b128289 · outbound

This paper cites In-context learning with iterative demonstration selection.

Exploring Imbalanced Annotations for Effective In-Context Learning In-context learning with iterative demonstration selection

Reference 47

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This paper cites UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation.

Exploring Imbalanced Annotations for Effective In-Context Learning UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generation

Reference 48

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Observation 75033bb4-b7d7-4832-9c13-037af01d6c60 · outbound

This paper cites In-context Learning with Retrieved Demonstrations for Language Models: A Survey.

Exploring Imbalanced Annotations for Effective In-Context Learning In-context Learning with Retrieved Demonstrations for Language Models: A Survey

Reference 49

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This paper cites C-ICL: Contrastive in-context learning for information extraction.

Exploring Imbalanced Annotations for Effective In-Context Learning C-ICL: Contrastive in-context learning for information extraction

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.571036Z

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Observation 8aaa08f1-31ef-471d-8d02-b6c3f85b1fed · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.

Exploring Imbalanced Annotations for Effective In-Context Learning Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity

Reference 51

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Observation 3b7029c7-9d64-427e-99e1-b19147947712 · outbound

This paper cites Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning

Reference 52

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This paper cites More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives.

Exploring Imbalanced Annotations for Effective In-Context Learning More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives

Reference 53

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This paper cites Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements.

Exploring Imbalanced Annotations for Effective In-Context Learning Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements

Reference 54

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Observation 8bf85827-3659-4def-90f5-6271cc552ea0 · outbound

This paper cites More samples or more prompts? exploring effective few-shot in-context learning for LLMs with in-context sampling.

Exploring Imbalanced Annotations for Effective In-Context Learning More samples or more prompts? exploring effective few-shot in-context learning for LLMs with in-context sampling

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.552756Z

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Observation 4cf0c8d3-d57e-4ae6-8001-9e0c19fbf546 · outbound

This paper cites Calibrate before use: Improving few-shot performance of language models.

Exploring Imbalanced Annotations for Effective In-Context Learning Calibrate before use: Improving few-shot performance of language models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.541833Z

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Observation 5666dd03-3aa8-4fe2-a9a5-e674da4184e3 · outbound

This paper cites How Robust are LLMs to In-Context Majority Label Bias?.

Exploring Imbalanced Annotations for Effective In-Context Learning How Robust are LLMs to In-Context Majority Label Bias?

Reference 57

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Observation 1aff218c-600b-4c08-af07-0e73b6f5ab9b · outbound

This paper cites Fine-tune Language Models to Approximate Unbiased In-context Learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Fine-tune Language Models to Approximate Unbiased In-context Learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T23:52:00.865170Z

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Observation 01ee0792-c905-404f-a2a5-6d69d911f826 · outbound

This paper cites Mixtures of In-Context Learners.

Exploring Imbalanced Annotations for Effective In-Context Learning Mixtures of In-Context Learners

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:52:01.097849Z

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Observation 65ed713f-db20-40a2-ad9a-03a167d4e666 · outbound

This paper cites Beyond Performance: Quantifying and Mitigating Label Bias in LLMs.

Exploring Imbalanced Annotations for Effective In-Context Learning Beyond Performance: Quantifying and Mitigating Label Bias in LLMs

Reference 60

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Observation ac70fcc3-9e22-4f6c-8cb8-0894ded26705 · outbound

This paper cites Large scale fine-grained categorization and domain-specific transfer learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Large scale fine-grained categorization and domain-specific transfer learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.531542Z

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Observation 2ba4a0b5-48c2-4868-a820-6aa2d60d89d2 · outbound

This paper cites Generalized test utilities for long-tail performance in extreme multi-label classification.

Exploring Imbalanced Annotations for Effective In-Context Learning Generalized test utilities for long-tail performance in extreme multi-label classification

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.521920Z

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Observation b327de13-abe6-4d80-be57-3ccb5e210b95 · outbound

This paper cites Equalization loss for long-tailed object recognition.

Exploring Imbalanced Annotations for Effective In-Context Learning Equalization loss for long-tailed object recognition

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.511765Z

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Observation d57cb068-479b-4d79-97b9-b82ed76e8dd4 · outbound

This paper cites Robust asymmetric loss for multi-label long-tailed learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Robust asymmetric loss for multi-label long-tailed learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.501328Z

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Observation 88b71a4e-4c54-4481-8722-eeb08987bb86 · outbound

This paper cites Robust loss function for class imbalanced semantic segmentation and image classification.

Exploring Imbalanced Annotations for Effective In-Context Learning Robust loss function for class imbalanced semantic segmentation and image classification

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.490929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 489bbba2-55a5-4bfc-bf9d-93c649a082cb · outbound

This paper cites Stochastic smoothing of the top-k calibrated hinge loss for deep imbalanced classification.

Exploring Imbalanced Annotations for Effective In-Context Learning Stochastic smoothing of the top-k calibrated hinge loss for deep imbalanced classification

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.479542Z

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Observation 949a35a8-75ca-448f-9840-4d63c05bacae · outbound

This paper cites Retrieval augmented classification for long-tail visual recognition.

Exploring Imbalanced Annotations for Effective In-Context Learning Retrieval augmented classification for long-tail visual recognition

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.467839Z

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Observation 8eea3171-a73a-436b-8aea-f38b70d805ac · outbound

This paper cites Lt-darts: An architectural approach to enhance deep long-tailed learning.

Exploring Imbalanced Annotations for Effective In-Context Learning Lt-darts: An architectural approach to enhance deep long-tailed learning

Reference 68

Resolution: verified exact; raw_fallback, observed 2026-08-08T23:52:01.071554Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d127f948-651c-47d8-abbb-db71fc620db3 · outbound

This paper cites Bayesian optimization with inequality constraints.

Exploring Imbalanced Annotations for Effective In-Context Learning Bayesian optimization with inequality constraints

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.456417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc837f31-7050-4aa6-9e75-ca81baf940f6 · outbound

This paper cites Bayesian Optimization: Open source constrained global optimization tool for Python, 2014.

Exploring Imbalanced Annotations for Effective In-Context Learning Bayesian Optimization: Open source constrained global optimization tool for Python, 2014

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.444454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3e4afee1-6bce-4e8a-bda9-5c254c3d563a · outbound

This paper cites OpenICL: An open-source framework for in-context learning.

Exploring Imbalanced Annotations for Effective In-Context Learning OpenICL: An open-source framework for in-context learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.432508Z

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Observation d8dc03a9-3789-4375-b939-c561374b46b9 · outbound

This paper cites Peft: State-of-the-art parameter-efficient fine-tuning methods.

Exploring Imbalanced Annotations for Effective In-Context Learning Peft: State-of-the-art parameter-efficient fine-tuning methods

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.420152Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e8a8ff84-693b-43dd-890e-01eed63d30a7 · outbound

This paper cites A Bayesian approach for prompt optimization in pre-trained language models.

Exploring Imbalanced Annotations for Effective In-Context Learning A Bayesian approach for prompt optimization in pre-trained language models

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:52:00.983421Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c904150e-9ac8-4f31-9065-50bc88b16994 · outbound

This paper cites Searching for optimal solutions with llms via bayesian optimization.

Exploring Imbalanced Annotations for Effective In-Context Learning Searching for optimal solutions with llms via bayesian optimization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.407532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4d6a5fe8-2bf9-4360-9c84-565d621b02a7 · outbound

This paper cites an unresolved cited work.

Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-08T23:52:01.395088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 52700bf7-bb7a-4d67-a746-4e9faca975c6 · outbound

This paper cites an unresolved cited work.

Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-08T23:52:01.381554Z

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Observation 6c15665e-b3a5-4c3f-9c9b-2b2ab9d45014 · outbound

This paper cites an unresolved cited work.

Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-08T23:52:01.369887Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d5434872-6976-4dc6-9ade-fa380eec26fb · outbound

This paper cites an unresolved cited work.

Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-08T23:52:01.358278Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6a3be3f-3b67-4c01-971b-f0111e7b8b92 · outbound

This paper cites an unresolved cited work.

Exploring Imbalanced Annotations for Effective In-Context Learning Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-08T23:52:01.346005Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d51ae3d1-f61b-4516-8ed6-76323f7c39ae · outbound

This paper cites func NewMessage() Message{return Message{Context: context.Background(),Headers: map[string]string{},Data: render.Data{},moot:&sync.RWMutex,}}.

Exploring Imbalanced Annotations for Effective In-Context Learning func NewMessage() Message{return Message{Context: context.Background(),Headers: map[string]string{},Data: render.Data{},moot:&sync.RWMutex,}}

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:52:01.335222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers