mlsanityData to 14 Oct 2026

Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing

arXiv 2209.14899 ↗First posted 29 Sep 2022cs.CLcited by 15

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Abstract (arXiv)

A common recent approach to semantic parsing augments sequence-to-sequence models by retrieving and appending a set of training samples, called exemplars. The effectiveness of this recipe is limited by the ability to retrieve informative exemplars that help produce the correct parse, which is especially challenging in low-resource settings. Existing retrieval is commonly based on similarity of query and exemplar inputs. We propose GandR, a retrieval procedure that retrieves exemplars for which outputs are also similar. GandRfirst generates a preliminary prediction with input-based retrieval. Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction. GandR sets the state of the art on multiple low-resource semantic parsing tasks.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 16 of its 19 references that are in the map. First posted on arXiv 29 Sep 2022.

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16 of 19 references.

References · 16

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  5. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  6. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  7. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  8. 2018Universal Sentence Encodercited by 489
  9. 2020MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmarkcited by 158
  10. 2021Case-based Reasoning for Natural Language Queries over Knowledge Basescited by 107
  11. 2020Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsingcited by 72
  12. 2022Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Datacited by 61
4 more
  1. 2020Conversational Semantic Parsingcited by 39
  2. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  3. 2021RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsingcited by 15
  4. 2022Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsingcited by 7

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Citing papers per year: 2022: 2; 2023: 7; 2024: 2; 2025: 4.

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  1. 2023Active Retrieval Augmented Generationcited by 516
  2. 2023RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generationcited by 367
  3. 2022In-context Examples Selection for Machine Translationcited by 172
  4. 2022Diverse Demonstrations Improve In-context Compositional Generalizationcited by 139
  5. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  6. 2023Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)cited by 72
  7. 2023CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translationcited by 12
  8. 2025Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generationcited by 10
  9. 2025Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learningcited by 7
  10. 2024Automatic Combination of Sample Selection Strategies for Few-Shot Learningcited by 5
  11. 2024A Statistical Framework for Data-dependent Retrieval-Augmented Modelscited by 3
  12. 2023The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challengecited by 1
3 more
  1. 2023Dynamic Retrieval-Augmented Generationcited by 1
  2. 2025Large Language Models are Demonstration Pre-Selectors for Themselvescited by 1
  3. 2025Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledgecited by 0