Summary of Code-mixer Ya Nahi: Novel Approaches to Measuring Multilingual Llms’ Code-mixing Capabilities, by Ayushman Gupta et al.
Code-Mixer Ya Nahi: Novel Approaches to Measuring Multilingual LLMs’ Code-Mixing Capabilities
by Ayushman Gupta, Akhil Bhogal, Kripabandhu Ghosh
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the capabilities of multilingual Large Language Models (LLMs) in Machine Translation (MT) tasks, specifically in the context of code-switching. The authors introduce Rule-Based Prompting, a novel technique for generating code-mixed sentences. They compare the MT abilities of three popular LLMs: GPT-3.5-turbo, GPT-4, and Gemini Pro across five language pairs (English-Hindi, Bengali, Gujarati, French, Spanish) using k-shot prompting and Rule-Based Prompting. The results show that while k-shot prompting often yields the best results, Rule-Based Prompting demonstrates promise in generating unique code-mixed sentences with varying styles of code-switching. Additionally, the authors create a gold-standard code-mixed dataset spanning five language pairs to evaluate the LLMs’ code-mixed-to-English translation abilities. The paper’s findings have implications for developing chatbots that can seamlessly switch between languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can translate between different languages, especially when people mix two or more languages in a single sentence. The authors developed a new way to get computers to generate these mixed-language sentences and tested it on three popular language models. They found that this new method works well, even when the computer only has limited training data. The researchers also created a special dataset to test how well the language models can translate code-mixed text back into English. This work could lead to more realistic chatbots that can handle conversations in multiple languages. |
Keywords
» Artificial intelligence » Gemini » Gpt » Prompting » Translation
