mlsanityData to 7 Oct 2026

Few-Shot Semantic Parsing with Language Models Trained On Code

arXiv 2112.08696 ↗First posted 16 Dec 2021cs.CLcited by 50

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Abstract (arXiv)

Large language models can perform semantic parsing with little training data, when prompted with in-context examples. It has been shown that this can be improved by formulating the problem as paraphrasing into canonical utterances, which casts the underlying meaning representation into a controlled natural language-like representation. Intuitively, such models can more easily output canonical utterances as they are closer to the natural language used for pre-training. Recently, models also pre-trained on code, like OpenAI Codex, have risen in prominence. For semantic parsing tasks where we map natural language into code, such models may prove more adept at it. In this paper, we test this hypothesis and find that Codex performs better on such tasks than equivalent GPT-3 models. We evaluate on Overnight and SMCalFlow and find that unlike GPT-3, Codex performs similarly when targeting meaning representations directly, perhaps because meaning representations are structured similar to code in these datasets.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

10 of 21 references.

References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2021Program Synthesis with Large Language Modelscited by 3,509
  4. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  5. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  6. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
  7. 2020Task-Oriented Dialogue as Dataflow Synthesiscited by 122
  8. 2020Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsingcited by 72
  9. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  10. 2021From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decodingcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 23; 2024: 7; 2025: 6; 2026: 1.

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  1. 2022PAL: Program-aided Language Modelscited by 635
  2. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 161
  3. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  4. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  5. 2024In-Context Language Learning: Architectures and Algorithmscited by 86
  6. 2023Grammar Prompting for Domain-Specific Language Generation with Large Language Modelscited by 58
  7. 2022Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsingcited by 50
  8. 2022In-Context Learning for Few-Shot Dialogue State Trackingcited by 47
  9. 2025Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlocited by 45
  10. 2022LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restrictioncited by 43
  11. 2022Controllable Dialogue Simulation with In-Context Learningcited by 42
  12. 2023On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codexcited by 41
38 more
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  2. 2022Calibrated Interpretation: Confidence Estimation in Semantic Parsingcited by 33
  3. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 32
  4. 2024ReGAL: Refactoring Programs to Discover Generalizable Abstractionscited by 32
  5. 2023Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Informationcited by 24
  6. 2025Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Samplingcited by 20
  7. 2023Zero and Few-shot Semantic Parsing with Ambiguous Inputscited by 16
  8. 2023"You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representationcited by 15
  9. 2023InstructExcel: A Benchmark for Natural Language Instruction in Excelcited by 14
  10. 2024Learning to Retrieve Iteratively for In-Context Learningcited by 13
  11. 2023Reliable Natural Language Understanding with Large Language Models and Answer Set Programmingcited by 12
  12. 2023Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogscited by 12
  13. 2023Generating Data for Symbolic Language with Large Language Modelscited by 12
  14. 2023Diverse Retrieval-Augmented In-Context Learning for Dialogue State Trackingcited by 10
  15. 2024Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRLcited by 10
  16. 2022NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queriescited by 8
  17. 2023Make a Choice! Knowledge Base Question Answering with In-Context Learningcited by 8
  18. 2023Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Promptingcited by 8
  19. 2022Paraphrasing Techniques for Maritime QA systemcited by 7
  20. 2022Analyzing Semantic Faithfulness of Language Models via Input Intervention on Question Answeringcited by 6
  21. 2023Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsingcited by 6
  22. 2022Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsingcited by 5
  23. 2023UINav: A Practical Approach to Train On-Device Automation Agentscited by 5
  24. 2024Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generationcited by 4
  25. 2025Non-Interactive Symbolic-Aided Chain-of-Thought for Logical Reasoningcited by 4
  26. 2023Chatbots As Fluent Polyglots: Revisiting Breakthrough Code Snippetscited by 3
  27. 2023On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Softwarecited by 3
  28. 2024Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Modelscited by 3
  29. 2025Do LLMs Encode Frame Semantics? Evidence from Frame Identificationcited by 3
  30. 2025LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real Worldcited by 2
  31. 2023Few-Shot Adaptation for Parsing Contextual Utterances with LLMscited by 1
  32. 2023Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Modelscited by 1
  33. 2025STARE at the Structure: Steering ICL Exemplar Selection with Structural Alignmentcited by 1
  34. 2022The Curious Case of Controlcited by 0
  35. 2023Semantic Parsing in Limited Resource Conditionscited by 0
  36. 2023Student as an Inherent Denoiser of Noisy Teachercited by 0
  37. 2024Inroads to a Structured Data Natural Language Bijection and the role of LLM annotationcited by 0
  38. 2026Is Code Better Than Language for Algorithmic Reasoningcited by 0