# Linguistic ambiguity analysis in ChatGPT

arXiv: https://arxiv.org/abs/2302.06426 · HTML: https://mlsanity.com/paper/2302.06426/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [A Survey on Contextual Embeddings](https://mlsanity.com/paper/2003.07278/) (2020)

## Built on by (most cited first, 14 of 14)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans](https://mlsanity.com/paper/2308.07462/) (2023)
- [Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie](https://mlsanity.com/paper/2309.09120/) (2023)
- [Humans are Still Better than ChatGPT: Case of the IEEEXtreme Competition](https://mlsanity.com/paper/2305.06934/) (2023)
- [Grammaticality Representation in ChatGPT as Compared to Linguists and Laypeople](https://mlsanity.com/paper/2406.11116/) (2024)
- [Gender Bias in Transformer Models: A comprehensive survey](https://mlsanity.com/paper/2306.10530/) (2023)
- [Generative AI and the future of scientometrics: current topics and future questions](https://mlsanity.com/paper/2507.00783/) (2025)
- [Evaluating ChatGPT on Medical Information Extraction Tasks: Performance, Explainability and Beyond](https://mlsanity.com/paper/2601.21767/) (2026)
- [An Exploration-Analysis-Disambiguation Reasoning Framework for Word Sense Disambiguation with Low-Parameter LLMs](https://mlsanity.com/paper/2603.05400/) (2026)
- [Evaluating Chinese Ambiguity Understanding in Large Language Models](https://mlsanity.com/paper/2605.15635/) (2026)
- [When a Name Is Not a Name: A Benchmark Dataset and Distilled Reasoning for Culturally Entangled Bangla Homographs in Low-Resource LLMs](https://mlsanity.com/paper/2607.17828/) (2026)

## Abstract (arXiv)

Linguistic ambiguity is and has always been one of the main challenges in Natural Language Processing (NLP) systems. Modern Transformer architectures like BERT, T5 or more recently InstructGPT have achieved some impressive improvements in many NLP fields, but there is still plenty of work to do. Motivated by the uproar caused by ChatGPT, in this paper we provide an introduction to linguistic ambiguity, its varieties and their relevance in modern NLP, and perform an extensive empiric analysis. ChatGPT strengths and weaknesses are revealed, as well as strategies to get the most of this model.