Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation

• URL: http://arxiv.org/abs/2304.01746v1
• Date: Tue, 4 Apr 2023 12:33:40 GMT
• Title: Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
• Authors: Tao Fang, Shu Yang, Kaixin Lan, Derek F. Wong, Jinpeng Hu, Lidia S. Chao, Yue Zhang
• Abstract summary: ChatGPT is a large-scale language model based on the advanced GPT-3.5 architecture. We design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT. Our evaluation involves assessing ChatGPT's performance on five official test sets in three different languages, along with three document-level GEC test sets in English.
• Score: 41.94480044074273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has shown remarkable potential in various Natural Language Processing (NLP) tasks. However, there is currently a dearth of comprehensive study exploring its potential in the area of Grammatical Error Correction (GEC). To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT. Our evaluation involves assessing ChatGPT's performance on five official test sets in three different languages, along with three document-level GEC test sets in English. Our experimental results and human evaluations demonstrate that ChatGPT has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent, possibly due to its over-correction tendencies and not adhering to the principle of minimal edits. Additionally, its performance in non-English and low-resource settings highlights its potential in multilingual GEC tasks. However, further analysis of various types of errors at the document-level has shown that ChatGPT cannot effectively correct agreement, coreference, tense errors across sentences, and cross-sentence boundary errors.

Related papers

• GPT-3.5 for Grammatical Error Correction [0.4757470449749875]
  This paper investigates the application of GPT-3.5 for Grammatical Error Correction (GEC) in multiple languages. We conduct automatic evaluations of the corrections proposed by GPT-3.5 using several methods. For English, GPT-3.5 demonstrates high recall, generates fluent corrections, and generally preserves sentence semantics. But, human evaluation for both English and Russian reveals that, despite its strong error-detection capabilities, GPT-3.5 struggles with several error types.
  arXiv  Detail & Related papers  (2024-05-14T09:51:09Z)
• Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction [60.32771192285546]
  ChatGPT has demonstrated impressive performance in various downstream tasks. In the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics. This paper proposes a new evaluation metric: Eval-GCSC. By incorporating word-level and semantic similarity judgments, it relaxes the stringent length and phonics constraints.
  arXiv  Detail & Related papers  (2023-11-14T14:56:33Z)
• Enhancing conversational quality in language learning chatbots: An evaluation of GPT4 for ASR error correction [20.465220855548292]
  This paper explores the use of GPT4 for ASR error correction in conversational settings. We find that transcriptions corrected by GPT4 lead to higher conversation quality, despite an increase in WER.
  arXiv  Detail & Related papers  (2023-07-19T04:25:21Z)
• ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning [70.57126720079971]
  Large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) This paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages.
  arXiv  Detail & Related papers  (2023-04-12T05:08:52Z)
• Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction [28.58384091374763]
  GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks. We perform experiments testing the capabilities of a GPT-3.5 model (text-davinci-003) and a GPT-4 model (gpt-4-0314) on major GEC benchmarks. We report the performance of our best prompt on the BEA-2019 and JFLEG datasets, finding that the GPT models can perform well in a sentence-level revision setting.
  arXiv  Detail & Related papers  (2023-03-25T03:08:49Z)
• ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark [11.36853733574956]
  ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI. We compare it with commercial GEC product (e.g., Grammarly) and state-of-the-art models (e.g., GECToR) We find that ChatGPT performs not as well as those baselines in terms of the automatic evaluation metrics.
  arXiv  Detail & Related papers  (2023-03-15T00:35:50Z)
• Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
  Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community. It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
  arXiv  Detail & Related papers  (2023-02-08T09:44:51Z)
• Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction [98.31440090585376]
  Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. Existing GEC models tend to produce spurious corrections or fail to detect lots of errors. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses.
  arXiv  Detail & Related papers  (2021-05-10T15:04:25Z)
• On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
  We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. Results confirm that the performance of all tested models is affected but the degree of impact varies.
  arXiv  Detail & Related papers  (2020-05-12T11:01:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.