mlsanityData to 7 Oct 2026

Finding Support Examples for In-Context Learning

arXiv 2302.13539 ↗First posted 27 Feb 2023cs.CLcited by 104

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Abstract (arXiv)

Additionally, the strong dependency among in-context examples makes it an NP-hard combinatorial optimization problem and enumerating all permutations is infeasible. Hence we propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages: First we filter the dataset to obtain informative in-context examples individually. Specifically, we propose a novel metric, InfoScore, to evaluate the example's in-context informativeness based on the language model's feedback, and further propose a progressive filtering process to filter out uninformative examples. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations, to find examples that fully depict the task. The experimental results show that LENS significantly outperforms a wide range of baselines.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Builds on 33 of its 65 references that are in the map. First posted on arXiv 27 Feb 2023.

Builds on

33 of 65 references.

References · 33

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  6. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  7. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  8. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  9. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  10. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  11. 2018An Empirical Study of Example Forgetting during Deep Neural Network Learningcited by 669
  12. 2021Learning To Retrieve Prompts for In-Context Learningcited by 641
21 more
  1. 2020A Survey of Deep Active Learningcited by 547
  2. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 529
  3. 2019Selection via Proxy: Efficient Data Selection for Deep Learningcited by 337
  4. 2021GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Trainingcited by 269
  5. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 242
  6. 2020GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learningcited by 224
  7. 2022Active Example Selection for In-Context Learningcited by 195
  8. 2021Noisy Channel Language Model Prompting for Few-Shot Text Classificationcited by 163
  9. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  10. 2022DeepCore: A Comprehensive Library for Coreset Selection in Deep Learningcited by 146
  11. 2023Compositional Exemplars for In-context Learningcited by 143
  12. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 136
  13. 2021Case-based Reasoning for Natural Language Queries over Knowledge Basescited by 107
  14. 2020The Turking Test: Can Language Models Understand Instructions?cited by 82
  15. 2022On the Relation between Sensitivity and Accuracy in In-context Learningcited by 71
  16. 2022ProGen: Progressive Zero-shot Dataset Generation via In-context Feedbackcited by 67
  17. 2022Data Curation Alone Can Stabilize In-context Learningcited by 58
  18. 2013Submodular Optimization with Submodular Cover and Submodular Knapsack Constraintscited by 40
  19. 2022Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learningcited by 36
  20. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 32
  21. 2021Core-set Sampling for Efficient Neural Architecture Searchcited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 25; 2024: 40; 2025: 28; 2026: 11.

104 papers.

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  1. 2023A Survey of Large Language Modelscited by 2,537
  2. 2023Efficient Large Language Models: A Surveycited by 183
  3. 2023Unified Demonstration Retriever for In-Context Learningcited by 141
  4. 2023Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communicationcited by 78
  5. 2023In-Context Learning with Iterative Demonstration Selectioncited by 74
  6. 2024In-context Learning with Retrieved Demonstrations for Language Models: A Surveycited by 69
  7. 2023Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMscited by 62
  8. 2023In-Context Learning Learns Label Relationships but Is Not Conventional Learningcited by 48
  9. 2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
  10. 2023One-Shot Learning as Instruction Data Prospector for Large Language Modelscited by 43
  11. 2025A Survey of Test-Time Compute: From Intuitive Inference to Deliberate Reasoningcited by 40
  12. 2023ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relationscited by 39
38 more
  1. 2023MoT: Memory-of-Thought Enables ChatGPT to Self-Improvecited by 37
  2. 2024Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplarscited by 37
  3. 2023Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selectioncited by 36
  4. 2024Knowledge Boundary of Large Language Models: A Surveycited by 36
  5. 2025Emergent Abilities in Large Language Models: A Surveycited by 34
  6. 2024Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMscited by 32
  7. 2023Large Language Model Instruction Following: A Survey of Progresses and Challengescited by 31
  8. 2024What Factors Affect Multi-Modal In-Context Learning? An In-Depth Explorationcited by 31
  9. 2023IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Modelscited by 26
  10. 2024Parallel Structures in Pre-training Data Yield In-Context Learningcited by 26
  11. 2024Retrieval Augmented Fact Verification by Synthesizing Contrastive Argumentscited by 25
  12. 2024Assistive Large Language Model Agents for Socially-Aware Negotiation Dialoguescited by 18
  13. 2024Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classificationcited by 18
  14. 2024Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentationcited by 17
  15. 2024Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performancecited by 17
  16. 2024Revisiting In-context Learning Inference Circuit in Large Language Modelscited by 16
  17. 2023Iterative Forward Tuning Boosts In-Context Learning in Language Modelscited by 15
  18. 2024On the Noise Robustness of In-Context Learning for Text Generationcited by 15
  19. 2023A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomnesscited by 14
  20. 2023Overview of the PromptCBLUE Shared Task in CHIP2023cited by 14
  21. 2024Misconfidence-based Demonstration Selection for LLM In-Context Learningcited by 14
  22. 2023Fine-tune Language Models to Approximate Unbiased In-context Learningcited by 12
  23. 2024Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Modelscited by 12
  24. 2025Sample Efficient Demonstration Selection for In-Context Learningcited by 12
  25. 2024In-Context Learning Demonstration Selection via Influence Analysiscited by 11
  26. 2024Strategic Demonstration Selection for Improved Fairness in LLM In-Context Learningcited by 10
  27. 2024Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Promptingcited by 10
  28. 2023SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQAcited by 9
  29. 2023Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learningcited by 9
  30. 2023Towards Informative Few-Shot Prompt with Maximum Information Gain for In-Context Learningcited by 9
  31. 2024Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learningcited by 9
  32. 2024Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Processcited by 9
  33. 2024Se2: Sequential Example Selection for In-Context Learningcited by 8
  34. 2024Relevant or Random: Can LLMs Truly Perform Analogical Reasoning?cited by 8
  35. 2024LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?cited by 8
  36. 2024Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for Classificationcited by 7
  37. 2025Meta-Prompt Optimization for LLM-Based Sequential Decision Makingcited by 7
  38. 2023Dynamic Demonstrations Controller for In-Context Learningcited by 6

50 most cited of 104 All 104 (JSON)