mlsanityData to 7 Oct 2026

Fumbling in Babel: An Investigation into ChatGPT's Language Identification Ability

arXiv 2311.09696 ↗First posted 16 Nov 2023cs.CLcited by 6

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Abstract (arXiv)

ChatGPT has recently emerged as a powerful NLP tool that can carry out a variety of tasks. However, the range of languages ChatGPT can handle remains largely a mystery. To uncover which languages ChatGPT `knows', we investigate its language identification (LID) abilities. For this purpose, we compile Babel-670, a benchmark comprising 670 languages representing 24 language families spoken in five continents. Languages in Babel-670 run the gamut from the very high-resource to the very low-resource. We then study ChatGPT's (both GPT-3.5 and GPT-4) ability to (i) identify language names and language codes (ii) under zero- and few-shot conditions (iii) with and without provision of a label set. When compared to smaller finetuned LID tools, we find that ChatGPT lags behind. For example, it has poor performance on African languages. We conclude that current large language models would benefit from further development before they can sufficiently serve diverse communities.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

15 of 30 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
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3 more
  1. 2022AfroLID: A Neural Language Identification Tool for African Languagescited by 38
  2. 2021Improved Language Identification Through Cross-Lingual Self-Supervised Learningcited by 20
  3. 2023Bhasha-Abhijnaanam: Native-script and romanized Language Identification for 22 Indic languagescited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.