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

arXiv: https://arxiv.org/abs/2209.14899 · HTML: https://mlsanity.com/paper/2209.14899/

## 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.

## Builds on (most cited first, 16 of 16)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark](https://mlsanity.com/paper/2008.09335/) (2020)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2010.03546/) (2020)
- [Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data](https://mlsanity.com/paper/2203.08773/) (2022)
- [Conversational Semantic Parsing](https://mlsanity.com/paper/2009.13655/) (2020)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2109.10410/) (2021)
- [Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2202.00901/) (2022)

## Built on by (most cited first, 15 of 15)

- [Active Retrieval Augmented Generation](https://mlsanity.com/paper/2305.06983/) (2023)
- [RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation](https://mlsanity.com/paper/2303.12570/) (2023)
- [In-context Examples Selection for Machine Translation](https://mlsanity.com/paper/2212.02437/) (2022)
- [Diverse Demonstrations Improve In-context Compositional Generalization](https://mlsanity.com/paper/2212.06800/) (2022)
- [Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory](https://mlsanity.com/paper/2305.02437/) (2023)
- [Automatic Semantic Augmentation of Language Model Prompts (for Code Summarization)](https://mlsanity.com/paper/2304.06815/) (2023)
- [CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation](https://mlsanity.com/paper/2305.14105/) (2023)
- [Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation](https://mlsanity.com/paper/2505.23657/) (2025)
- [Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning](https://mlsanity.com/paper/2506.09473/) (2025)
- [Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://mlsanity.com/paper/2402.03038/) (2024)
- [A Statistical Framework for Data-dependent Retrieval-Augmented Models](https://mlsanity.com/paper/2408.15399/) (2024)
- [The Pipeline System of ASR and NLU with MLM-based Data Augmentation toward STOP Low-resource Challenge](https://mlsanity.com/paper/2305.01194/) (2023)
- [Dynamic Retrieval-Augmented Generation](https://mlsanity.com/paper/2312.08976/) (2023)
- [Large Language Models are Demonstration Pre-Selectors for Themselves](https://mlsanity.com/paper/2506.06033/) (2025)
- [Handling Open-Vocabulary Constructs in Formalizing Specifications: Retrieval-Augmented Parsing with Expert Knowledge](https://mlsanity.com/paper/2509.08808/) (2025)

## 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.