# Does Correction Remain A Problem For Large Language Models?

arXiv: https://arxiv.org/abs/2308.01776 · HTML: https://mlsanity.com/paper/2308.01776/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [A Simple Recipe for Multilingual Grammatical Error Correction](https://mlsanity.com/paper/2106.03830/) (2021)
- [Parallel Iterative Edit Models for Local Sequence Transduction](https://mlsanity.com/paper/1910.02893/) (2019)
- [Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding](https://mlsanity.com/paper/2106.04970/) (2021)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions Towards Knowledge Graph Chatbots](https://mlsanity.com/paper/2302.06466/) (2023)

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Contextual Spelling Correction with Language Model for Low-resource Setting](https://mlsanity.com/paper/2404.18072/) (2024)
- [LMSpell: Spell Correction with Pre-Trained Language Models](https://mlsanity.com/paper/2512.05414/) (2025)

## Abstract (arXiv)

As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist? This paper investigates the role of correction in the context of large language models by conducting two experiments. The first experiment focuses on correction as a standalone task, employing few-shot learning techniques with GPT-like models for error correction. The second experiment explores the notion of correction as a preparatory task for other NLP tasks, examining whether large language models can tolerate and perform adequately on texts containing certain levels of noise or errors. By addressing these experiments, we aim to shed light on the significance of correction in the era of large language models and its implications for various NLP applications.