Beyond Standard Greek: GRDD+ Turns Dialects into Data to Supercharge Language Models

Beyond Standard Greek: GRDD+ Turns Dialects into Data to Supercharge Language Models

If you’ve ever tried to train a language model to understand a language that isn’t spoken the same everywhere, you know the challenge: dialects, regional vocabulary, and even orthography can throw a curveball at every AI system. Greek, with its rich tapestry of regional varieties—from Cretan and Pontic to Maniot and Tsakonian—offers a perfect case study. A new dataset project, GRDD+, steps into this space not just to map the nuance of Greek dialects but to test how well modern language models can adapt when they’re taught with high-quality dialect data. The result is a major resource for researchers and developers who want AI that truly “gets” local language variation.

In this blog post, I’ll unpack what GRDD+ is, why it matters, what the researchers did to test it across different model architectures, what they found, and what it could mean for real-world applications—from better dialect-aware chatbots to resources that help preserve endangered linguistic varieties. I’ll wrap up with practical takeaways you can apply, whether you’re a researcher, an educator, or a developer curious about dialect-aware NLP.

What GRDD+ is and why it matters

Grounded in the Modern Greek dialect landscape, GRDD+ is an extended dialectal dataset designed to push AI beyond the standard Modern Greek we often see in language models. The core idea is simple and powerful: feed models high-quality dialect data so they can learn regional speech patterns, vocabulary, and even stylistic quirks that a standard dataset would miss.

Key facts about GRDD+:
- It expands the existing GRDD dataset by adding six more dialect varieties: Greco-Corsican, Griko (a southern Italian Greek variety), Maniot, Heptanesian, Tsakonian, and Katharevusa Greek.
- The total corpus now amounts to about 6,374,939 words across 10 Greek dialect varieties.
- The extended dataset complements the original four GRDD dialects (Cretan, Pontic, Northern Greek, Cypriot) and broadens the linguistic landscape researchers can study.
- Data sources include freely available web texts (blogs, websites, public literary works) plus digitized and OCR-derived material from books, especially for the minority varieties.
- The dataset is designed to be openly usable, with code and data access described by the authors.

Why this matters for AI and NLP
- Dialects are often underserved in NLP, especially for lower-resource languages or highly regional varieties. LLMs can do a decent job “zero-shot” or with a little fine-tuning, but cross-dialect handling—getting the nuances of multiple regional varieties right—remains challenging.
- GRDD+ addresses this by providing a sizable, diverse, high-quality dialect corpus that enables targeted fine-tuning, making it possible to see how much dialect data helps across different architectures.
- For real-world apps, this could translate into more natural virtual assistants, better automated translation, more accurate part-of-speech tagging and parsing for dialect texts, and tools that help document or preserve endangered speech varieties.

How the GRDD+ dataset was built (in plain terms)

The authors didn’t just throw a bunch of dialect sentences into a dataset and call it a day. They followed a thoughtful data-collection and preprocessing pipeline:

• Data sources: Texts from blogs, public websites, songs, poems, folktales, dialogues, and translated works. For several minority varieties (Greco-Corsican, Griko, Heptanesian, Maniot, Pontic), OCR from books was used to capture additional dialect material, with metadata stripped away to keep the focus on dialect text itself.
• Cleaning and standardization: Basic cleanup steps like removing numbers, URLs, duplicate lines, and extra whitespace to ensure the data is as clean as possible for model training.
• Enrichment of GRDD: The GRDD+ dataset adds six lesser-represented Greek dialects to the four already in GRDD, providing a broader spectrum of dialectal features to learn from.
• Size and variety balance: The expanded dataset includes a mix of regional speech patterns, historical influences, and loanwords (for example, Venetian/Italian influences in Heptanesian and related varieties). This helps models learn not just local vocabulary but also how different dialects borrow and adapt words.

Fine-tuning across multiple model architectures (the “cross-architecture” part)

One of the most interesting aspects of GRDD+ is how the authors tested its usefulness across different AI architectures. They didn’t settle for a single “best” model; instead, they ran fine-tuning experiments on three different open-weight LLMs and compared them with frontier (top-tier) models.

Model architectures used for fine-tuning:
- Llama-3-8B
- Llama-3.1-8B (an enhanced context version)
- Krikri-8B (a Greek-focused model built on the Llama family)

For comparison with broader AI leaders, they evaluated against frontier models such as Claude-3.7-Sonnet, Gemini-2.5, and ChatGPT-5. The goal was to see not only how the dialect data helps smaller, open-weight models but also how those improvements stack up against top commercial models.

Training setup and approach (in approachable terms)
- They used a structured fine-tuning approach designed to be efficient and dialect-aware. A technique called LoRA (Low-Rank Adaptation) was employed to make fine-tuning more practical on 8B-scale models. In other words, they could adapt models to Greek dialects without needing to retrain the entire network from scratch.
- Data presentation: They split training data into manageable chunks (around 100 words per chunk) and used prompt-completion style pairs to nudge the models toward producing dialect-consistent output. Longer prompts and a variety of prompts (seven distinct prompt styles covering genres like news, dialogues, and creative writing) were used to expose models to different dialect contexts.
- Generative evaluation: The authors assessed how well the models could generate dialect-consistent text and how their dialect handling improved across different varieties, not just in one or two of them.
- Baseline comparisons: In addition to the three open-weight models, the study compared performance against frontier models to understand how close the open models could get to top-tier performance when dialect data is involved.

What the results suggest (the big-picture takeaways)

• Dialect data helps across the board: Fine-tuning with GRDD+ data consistently improved performance over the base versions of the models. The gains were observed across multiple dialects, not just a single variety, underscoring the value of broad dialect exposure.
• Smaller models can catch up with the right data: Some 8B-scale models benefited particularly from the dialect data in terms of robustness and cross-dialect generalization. In several cases, the fine-tuned open-weight models approached or exceeded the baseline performance of larger, more powerful frontier models on dialect-related tasks.
• Greek-specific models show promise: The Krikri-8B model, which is tailored toward Greek, performed well in dialect-specific evaluations, sometimes outperforming generic fine-tuned variants on certain dialect tasks. This suggests that models with language-niche alignments (think of a model pre-configured around a language family or script) can leverage dialect data more effectively.
• The value of high-quality, diverse data: The study highlighted that data quality (not just volume) matters. The GRDD+ contributions—especially the minority varieties added with careful data curation and OCR-derived content—made a noticeable difference in how well the models could capture dialectal nuance.
• The importance of prompt design and training setup: The researchers experimented with different prompt structures and training recipe variations. They found that a mix of prompt styles, longer prompts, and carefully chosen training exemplars helped the models better grasp dialectal texture (for example, variations in syntax, morphology, and vocabulary usage across dialects).

Limitations and caveats (honest, practical wisdom)

• Data balance and coverage: While GRDD+ expands the dialect landscape, some varieties still have relatively fewer examples than others. This imbalance can influence model performance and the reliability of cross-dialect generalization metrics.
• Model scale and domain gaps: Even though fine-tuned 8B models showed strong gains, there are limits to how far small-to-medium models can go in matching the nuance of large frontier models, especially when evaluating sophisticated dialect phenomena or long-form generation.
• Evaluation challenges: Assessing dialect quality is inherently tricky. The study used structured prompts and cross-dialect tests, but shared, standardized metrics for dialectal NLP remain an ongoing area of development. As such, some of the reported gains are relative to chosen evaluation schemes.
• Data provenance and licensing: The dataset relies on freely available sources and OCR-augmented texts, which is great for openness, but readers should pay attention to licensing for downstream commercial use and to ensure proper attribution where required.
• Future directions suggested by the authors: They see value in extending evaluation to more dialects, exploring larger model variants, and developing automated evaluation tools that better capture dialectal quality and naturalness. They also discuss potential experiments like task-based evaluations (e.g., dialect-aware translation, annotation, or dialogue generation) to stress-test the fine-tuned models.

Practical implications and real-world applications

If you’re building Greek-language tech or if you’re involved in digital humanities or language preservation, GRDD+ offers a meaningful lever:

• Dialect-aware AI assistants and chatbots: With better handling of regional speech, a Greek-speaking assistant could respond in the local dialect appropriate to a user’s context, making interactions feel more natural and respectful of regional variation.
• Improved dialect identification and NLP tooling: POS tagging, syntactic parsing, or named-entity recognition trained with dialect-aware data could be more reliable across regions, improving downstream workflows in education, governance, and media.
• Language preservation and education: GRDD+ provides a dataset that can be used to study how dialects evolve, document minority varieties, and create educational materials that reflect the real linguistic landscape of modern Greece and surrounding Greek-speaking communities.
• Cross-lingual and loanword studies: For researchers interested in language contact, the minority varieties in GRDD+ (with Lazio- and Venetian-influenced dialects, loans, and calques) offer fertile ground for studying how Greek interacts with neighboring languages and cultures.

A note on future work and ongoing exploration

The authors are explicit about continuing the work. Some anticipated directions include:
- Testing larger, more capable “fine-tuned” models and more diverse prompt strategies to push dialect comprehension even further.
- Extending the dataset with more minority dialects and possibly exploring multi-dialect generation tasks (like mixed-dialect dialogue or translation that can shift registers).
- Investigating automated evaluation methods and more robust cross-dialect benchmarks to better quantify gains and guide further improvements.

Key takeaways

• GRDD+ is a major expansion of Greek dialect data, bringing together ten dialects and nearly 6.4 million words to the NLP community, with six new varieties added to the original GRDD.
• The study rigorously tests fine-tuning across three open-weight model architectures (Llama-3-8B, Llama-3.1-8B, Krikri-8B) and benchmarks them against frontier models, highlighting how dialect data can raise performance even for smaller models.
• Data quality and diversity matter: carefully curated dialect content, including OCR-derived material for minority varieties, helps models learn authentic dialect features and improves cross-dialect performance.
• Prompt design and training setup have tangible effects: long prompts, varied prompt types, and structured prompt-completion training help models better handle dialectal nuance.
• Practical impact spans education, digital humanities, and everyday tech: more dialect-aware NLP could lead to better virtual assistants, more accurate translation tools, and resources that support language preservation.
• There are clear limits and opportunities for improvement: data balance across dialects, evaluation standards, and exploring even larger or more specialized models will be important steps forward.

If you’re curious about practical experiments, a possible next move is to try fine-tuning an open-model on GRDD+ dialects for a small project—perhaps a dialect-aware chatbot or a dialect-variant tagging tool for Greek text. The dataset and tuning approaches are designed to be accessible, with the aim of letting more researchers and developers explore how to bridge the gap between broad AI capabilities and the rich, local texture of human language.

Key Takeaways (short version)
- GRDD+ dramatically broadens Modern Greek dialect resources, enabling more nuanced NLP across 10 dialect varieties.
- Fine-tuning across multiple model architectures shows dialect data can meaningfully improve cross-dialect performance, especially for smaller models.
- High-quality, diverse dialect data—including minority varieties—produces better generalization and keeps dialectal features from being overlooked.
- Prompt design and tuning strategies matter; varied prompts and longer context help models capture dialectal texture more effectively.
- The work has practical implications for dialect-aware apps and language preservation, with open datasets that invite broader experimentation and collaboration.
- Limitations like data imbalance and evaluation challenges point to fruitful avenues for future improvements and broader community involvement.