---
title: "Pushing the Limits of ChatGPT on NLP Tasks — Sun 2023"
description: "When used through in-context prompting, ChatGPT usually lags behind fully supervised models on… 172 references, cited by 19 papers here."
authors: ["Xiaofei Sun", "Linfeng Dong", "Xiaoya Li", "Zhen Wan", "Shuhe Wang", "Tianwei Zhang", "Jiwei Li", "Fei Cheng", "Lingjuan Lyu", "Fei Wu", "Guoyin Wang"]
year: 2023
venue: "arXiv:2306.09719"
source: "https://arxiv.org/abs/2306.09719"
doi: "10.48550/arxiv.2306.09719"
arxiv: "2306.09719"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sun-2023-pushing-limits-chatgpt-nlp-tasks/"
---
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# Pushing the Limits of ChatGPT on NLP Tasks

**Authors:** [Xiaofei Sun](https://md.research.elamur.ai/free/en/authors/xiaofei-sun/), [Linfeng Dong](https://md.research.elamur.ai/free/en/authors/linfeng-dong/), [Xiaoya Li](https://md.research.elamur.ai/free/en/authors/xiaoya-li/), [Zhen Wan](https://md.research.elamur.ai/free/en/authors/zhen-wan/), [Shuhe Wang](https://md.research.elamur.ai/free/en/authors/shuhe-wang/), [Tianwei Zhang](https://md.research.elamur.ai/free/en/authors/tianwei-zhang/), [Jiwei Li](https://md.research.elamur.ai/free/en/authors/jiwei-li/), [Fei Cheng](https://md.research.elamur.ai/free/en/authors/fei-cheng/), Lingjuan Lyu, [Fei Wu](https://md.research.elamur.ai/free/en/authors/fei-qing-wu/) and [Guoyin Wang](https://md.research.elamur.ai/free/en/authors/guoyin-wang/) · **Year:** 2023 · **Venue:** arXiv:2306.09719 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2306.09719) · [PDF](https://arxiv.org/pdf/2306.09719) · [DOI 10.48550/arxiv.2306.09719](https://doi.org/10.48550/arxiv.2306.09719) · [arXiv 2306.09719](https://arxiv.org/abs/2306.09719) · [OpenAlex](https://openalex.org/W4381248030)

**Citations:** 9 citations (OpenAlex, 2026-10-14) · [19](https://md.research.elamur.ai/free/en/papers/sun-2023-pushing-limits-chatgpt-nlp-tasks/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
When used through in-context prompting, ChatGPT usually lags behind fully supervised models on standard NLP benchmarks. The authors trace this gap to three causes. The prompt length cap leaves most labeled data unused. Many tasks, such as tagging or parsing, do not fit free-form generation well. The model also has built-in flaws, such as assigning labels to inputs that have none and over-relying on surface keywords. To address these, they assemble six general-purpose modules:
- spreading demonstrations across several prompts per input and voting on the answers
- choosing demonstrations by kNN search over a fine-tuned encoder's representations
- recasting tasks into generation-friendly formats
- adding rationales that ChatGPT writes for every training example
- a self-verification pass
- paraphrasing for robustness

Tested on 21 datasets spanning 10 tasks, from QA and NLI to NER, dependency parsing, SRL and POS tagging, the combined pipeline matches or surpasses supervised baselines on most datasets and sometimes reaches state-of-the-art results. The work shows that systematic prompt design can largely close the gap between a general LLM and task-specific models.

## Key points
- The 4,096-token input limit is the main bottleneck. The one-input-multiple-prompts method works around it by fusing N separate predictions through voting.
- Retrieving demonstrations with a fine-tuned RoBERTa outperformed both SimCSE and random selection. Sentence-level tasks use sentence representations and token-level tasks use token representations.
- Extraction tasks such as NER and dependency parsing are reframed as copying the input and wrapping target tokens in `##…@@` markers. Multi-class tasks are split into N yes/no questions, except POS tagging, where 45 tags make that too costly.
- On SRL (CoNLL2009, CoNLL2012), multiple prompts added +0.4 for predicate disambiguation and +0.3 for argument labeling.
- Overall, ChatGPT matched or beat supervised RoBERTa on 17 of the 21 datasets.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Xiaofei Sun, Linfeng Dong, Xiaoya Li, Zhen Wan, Shuhe Wang, Tianwei Zhang, Jiwei Li, Fei Cheng, Lingjuan Lyu, Fei Wu and Guoyin Wang. 2023. Pushing the Limits of ChatGPT on NLP Tasks. arXiv:2306.09719.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sun2023pushing,
  title = {{Pushing the Limits of ChatGPT on NLP Tasks}},
  author = {Xiaofei Sun, Linfeng Dong, Xiaoya Li, Zhen Wan, Shuhe Wang, Tianwei Zhang, Jiwei Li, Fei Cheng, Lingjuan Lyu, Fei Wu and Guoyin Wang},
  year = {2023},
  doi = {10.48550/arxiv.2306.09719},
  eprint = {2306.09719},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2306.09719}
}
```

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*Updated 30 October 2026*