Summary of Prompting with Phonemes: Enhancing Llms’ Multilinguality For Non-latin Script Languages, by Hoang H Nguyen et al.
Prompting with Phonemes: Enhancing LLMs’ Multilinguality for Non-Latin Script Languages
by Hoang H Nguyen, Khyati Mahajan, Vikas Yadav, Julian Salazar, Philip S. Yu, Masoud Hashemi, Rishabh Maheshwary
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper explores the limitations of multilingual language models (LLMs) in non-Latin script languages, such as Chinese or Arabic. Despite their impressive performance on benchmarks, LLMs struggle to adapt to these languages due to their pre-training with Latin-based scripts. The authors propose incorporating phonemic transcriptions as an additional signal to create script-invariant representations, which improves performance across both Latin and non-Latin script languages. Specifically, the study shows that integrating phonemic signals can boost performance by up to 12.6% for Latin script languages and up to 15.1% for non-Latin script languages compared to a randomized in-context learning (ICL) retrieval strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how language models are not good at understanding certain languages that don’t use the same alphabet as English. Despite being very smart, these models struggle to learn from languages like Chinese or Arabic because they were trained on Latin-based scripts. The researchers found a way to make the models better by adding another type of information called phonemic transcriptions. This helps the models understand both Latin and non-Latin script languages more accurately. The study shows that this new approach can improve performance by up to 15% for certain languages. |
