Antonios Dimakis
@antoniosdimakis.bsky.social
PhD fellow at Archimedes Unit, Athena Research Center | PhD student at the National and Kapodistrian University of Athens
Interested in NLP for low-resource languages/terms, tokenization, and linguistics
Interested in NLP for low-resource languages/terms, tokenization, and linguistics
Proud to work with John Pavlopoulos and @antonisa.bsky.social on this publication!
Check out the data and code here: github.com/andhmak/rule...
4/4
Check out the data and code here: github.com/andhmak/rule...
4/4
GitHub - andhmak/rule_dialnorm: Code and datasets associated with the paper titled "Dialect Normalization using Large Language Models and Morphological Rules"
Code and datasets associated with the paper titled "Dialect Normalization using Large Language Models and Morphological Rules" - GitHub - andhmak/rule_dialnorm: Code and datasets associa...
github.com
July 25, 2025 at 5:52 PM
Proud to work with John Pavlopoulos and @antonisa.bsky.social on this publication!
Check out the data and code here: github.com/andhmak/rule...
4/4
Check out the data and code here: github.com/andhmak/rule...
4/4
We implement our method for Greek, and experiment on a proverb dataset. We therefore very cheaply extend NLU coverage of models pre-trained on just the standard to almost every Greek dialect.
After normalizing we even find cultural insights which were previously obscured!
3/4
After normalizing we even find cultural insights which were previously obscured!
3/4
July 25, 2025 at 5:50 PM
We implement our method for Greek, and experiment on a proverb dataset. We therefore very cheaply extend NLU coverage of models pre-trained on just the standard to almost every Greek dialect.
After normalizing we even find cultural insights which were previously obscured!
3/4
After normalizing we even find cultural insights which were previously obscured!
3/4
"Dialect Normalization using Large Language Models and Morphological Rules"
By applying rule-based, linguistically informed transformations to the input before passing it to a LLM, with targeted few-shot prompting, we can obtain high-quality normalized outputs.
2/4
By applying rule-based, linguistically informed transformations to the input before passing it to a LLM, with targeted few-shot prompting, we can obtain high-quality normalized outputs.
2/4
July 25, 2025 at 5:49 PM
"Dialect Normalization using Large Language Models and Morphological Rules"
By applying rule-based, linguistically informed transformations to the input before passing it to a LLM, with targeted few-shot prompting, we can obtain high-quality normalized outputs.
2/4
By applying rule-based, linguistically informed transformations to the input before passing it to a LLM, with targeted few-shot prompting, we can obtain high-quality normalized outputs.
2/4