# Multilingual Tourist Assistance using ChatGPT: Comparing Capabilities in Hindi, Telugu, and Kannada

arXiv: https://arxiv.org/abs/2307.15376 · HTML: https://mlsanity.com/paper/2307.15376/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [Neural Machine Translation: A Review and Survey](https://mlsanity.com/paper/1912.02047/) (2019)
- [PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India](https://mlsanity.com/paper/2305.08828/) (2023)

## Built on by (most cited first, 2 of 2)

- [Decoding the Diversity: A Review of the Indic AI Research Landscape](https://mlsanity.com/paper/2406.09559/) (2024)
- [Whose Journey Matters? Investigating Identity Biases in Large Language Models (LLMs) for Travel Planning Assistance](https://mlsanity.com/paper/2410.17333/) (2024)

## Abstract (arXiv)

This research investigates the effectiveness of ChatGPT, an AI language model by OpenAI, in translating English into Hindi, Telugu, and Kannada languages, aimed at assisting tourists in India's linguistically diverse environment. To measure the translation quality, a test set of 50 questions from diverse fields such as general knowledge, food, and travel was used. These were assessed by five volunteers for accuracy and fluency, and the scores were subsequently converted into a BLEU score. The BLEU score evaluates the closeness of a machine-generated translation to a human translation, with a higher score indicating better translation quality. The Hindi translations outperformed others, showcasing superior accuracy and fluency, whereas Telugu translations lagged behind. Human evaluators rated both the accuracy and fluency of translations, offering a comprehensive perspective on the language model's performance.