mlsanityData to 14 Oct 2026

Infusing Prompts with Syntax and Semantics

arXiv 2412.06107 ↗First posted 8 Dec 2024cs.CLcited by 2

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Abstract (arXiv)

Despite impressive success, language models often generate outputs with flawed linguistic structure. We analyze the effect of directly infusing various kinds of syntactic and semantic information into large language models. To demonstrate the value of our proposals, we focus on the translation of natural language queries to SQL, in particular dealing with languages with less resources than English, to better investigate how much help we can get from low cost syntactic and semantic information. We show that linguistic analysis can significantly boost language models, to the point that we have surpassed previous best systems.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 8 Dec 2024.

Builds on

14 of 37 references.

References · 14

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  6. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  7. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 206
  8. 2017Learning a Neural Semantic Parser from User Feedbackcited by 99
  9. 2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 68
  10. 2021Syntax-BERT: Improving Pre-trained Transformers with Syntax Treescited by 51
  11. 2022DALL-E 2 Fails to Reliably Capture Common Syntactic Processescited by 31
  12. 2021Structural Guidance for Transformer Language Modelscited by 25
2 more
  1. 2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
  2. 2022Structural generalization is hard for sequence-to-sequence modelscited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilitiescited by 3
  2. 2026Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expandingcited by 1