Summary of Egalitarian Language Representation in Language Models: It All Begins with Tokenizers, by Menan Velayuthan and Kengatharaiyer Sarveswaran
Egalitarian Language Representation in Language Models: It All Begins with Tokenizers
by Menan Velayuthan, Kengatharaiyer Sarveswaran
First submitted to arxiv on: 17 Sep 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 investigates the role of tokenizers in adapting English-Centric Large Language Models (LLMs) to other languages, particularly those with complex scripts such as Tamil, Sinhala, and Hindi. The authors demonstrate that pre-tokenization methods can influence the representation of these languages in language models, potentially leading to unfair representation. They propose an improvement to the Byte Pair Encoding (BPE) algorithm, incorporating graphemes into a new approach called Grapheme Pair Encoding (GPE). Experiments show that GPE outperforms byte-level tokenizers for complex scripts. This work has implications for the development of language models and their applications in multilingual contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making sure that computer programs can understand languages from around the world, like Tamil and Hindi. Right now, most language programs are designed only for English, but this paper shows that they’re not very good at understanding other languages. The authors explain why this is happening and propose a new way to do things called Grapheme Pair Encoding (GPE). They test GPE on three languages and find that it works better than the old way. This could help make language programs more helpful for people who speak different languages. |
Keywords
» Artificial intelligence » Tokenization
