Evaluating Dedicated Monolingual and Joint Multilingual Causal Models for Dravidian Languages
Dravidian languages, mainly Tamil, Telugu, Kannada, and Malayalam make up only a small part of the data used to train multilingual language models, so it's not clear how much per-language ability these models actually keep. I have trained five GPT-2 architecture models from scratch to compare four monolingual models (one each for Tamil, Telugu, Kannada, and Malayalam, each with its own 32K-vocabulary subword tokenizer) against one multilingual model sharing a 64K-vocabulary subword tokenizer across all four languages. All the 5 models are trained on cleaned CC-100, Wikipedia, and Samanantar data. I have tested the models on perplexity, bits-per-byte, tokenizer efficiency, and fine-tuning ...
| Authors | Venkata Naga Sai Vishnu Rohit Pulipaka |
|---|---|
| Primary category | cs.CL |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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