mlsanityData to 14 Oct 2026

ChatGPT is a Potential Zero-Shot Dependency Parser

arXiv 2310.16654 ↗First posted 25 Oct 2023cs.CLcited by 5

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Abstract (arXiv)

Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance. However, it remains an understudied question whether pre-trained language models can spontaneously exhibit the ability of dependency parsing without introducing additional parser structure in the zero-shot scenario. In this paper, we propose to explore the dependency parsing ability of large language models such as ChatGPT and conduct linguistic analysis. The experimental results demonstrate that ChatGPT is a potential zero-shot dependency parser, and the linguistic analysis also shows some unique preferences in parsing outputs.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 25 Oct 2023.

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11 of 26 references.

References · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  5. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  6. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  7. 2016Graph-Structured Representations for Visual Question Answeringcited by 123
  8. 2020Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERTcited by 109
  9. 2018Stack-Pointer Networks for Dependency Parsingcited by 36
  10. 2019Enhancing Machine Translation with Dependency-Aware Self-Attentioncited by 24
  11. 2021Headed-Span-Based Projective Dependency Parsingcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.

5 papers.

  1. 2023How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questionscited by 7
  2. 2024Annotating FrameNet via Structure-Conditioned Language Generationcited by 2
  3. 2025Better Benchmarking LLMs for Zero-Shot Dependency Parsingcited by 2
  4. 2025Enhancing Korean Dependency Parsing with Morphosyntactic Featurescited by 1
  5. 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Modelscited by 1