Evaluating Telugu Proficiency in Large Language Models_ A Comparative Analysis of ChatGPT and Gemini

Katikela Sreeharsha Kishore, Rahimanuddin Shaik
4/30/2024
cs.CLcs.HC

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

The growing prominence of large language models (LLMs) necessitates the exploration of their capabilities beyond English. This research investigates the Telugu language proficiency of ChatGPT and Gemini, two leading LLMs. Through a designed set of 20 questions encompassing greetings, grammar, vocabulary, common phrases, task completion, and situational reasoning, the study delves into their strengths and weaknesses in handling Telugu. The analysis aims to identify the LLM that demonstrates a deeper understanding of Telugu grammatical structures, possesses a broader vocabulary, and exhibits superior performance in tasks like writing and reasoning. By comparing their ability to comprehend and use everyday Telugu expressions, the research sheds light on their suitability for real-world language interaction. Furthermore, the evaluation of adaptability and reasoning capabilities provides insights into how each LLM leverages Telugu to respond to dynamic situations. This comparative analysis contributes to the ongoing discussion on multilingual capabilities in AI and paves the way for future research in developing LLMs that can seamlessly integrate with Telugu-speaking communities.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{kishore2024evaluating,
  title  = {Evaluating Telugu Proficiency in Large Language Models_ A Comparative Analysis of ChatGPT and Gemini},
  author = {Katikela Sreeharsha Kishore and Rahimanuddin Shaik},
  year   = {2024},
  eprint = {2404.19369},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.19369v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.19369.svg)](https://codeofpaper.com/paper/2404.19369)

Discussion