High Token Inflation and Lack of Representation for Bangladeshi Bengali

(Main Script in Bangla Language, that translated and point-outed via ai. in our country, written more articles on that topic and they throwing concerns)

Dear AI Alignment and Dataset Operations Team,

I am writing to report a critical issue regarding regional bias and structural disparities in your Bengali language processing pipelines, tokenization algorithms, and default vocabulary outputs.

While Bangladesh accounts for over 170 million of the world's 280+ million native Bengali speakers, your current models systematically default to West Bengal (Indian) dialectal variants and spellings, treating the official Standard Bengali of Bangladesh as an outlier.

Key Technical Issues Identified:

Regional Alignment Bias: The model repeatedly defaults to Kolkata/West Bengal vocabulary (e.g., using 'জলখাবার', 'স্নান', 'কাকা') over the official Standard Bengali defined by the Bangla Academy of Bangladesh (e.g., 'নাশতা', 'গোসল', 'চাচা').

Tokenization and Cost Inefficiency: Due to the dominance of Latin-centric tokenizers, Bengali text requires up to 6 times more tokens compared to English for equivalent semantic content. This creates severe token-limit constraints and inflates API/subscription costs for Bangladeshi users.

Underrepresented Training Data: Your training pipeline heavily relies on open-web scrapings dominated by Indian news portals and pre-1947 archives, severely lacking machine-readable datasets from Bangladesh's public documents, legal records, and national archives.

Requested Action Items:

1. Dataset Integration: Incorporate open-access datasets, national corpora, and digitized text adhering to the Bangla Academy Standard into your base training and fine-tuning (RLHF) pipelines.

2. Regional Tokenizer Optimization: Allocate dedicated vocabulary slots for Bengali characters and sub-words in future tokenizer iterations to reduce token cost and improve context window capacity.

3. Equitable Default Alignment: Update system prompts and regional guardrails so that queries originating from or targeted at Bangladesh default to the official Bangladeshi Standard Bengali.

Bangladesh represents the vast majority of native Bengali speakers globally. Ensuring fair linguistic representation is essential for algorithmic fairness, cultural accuracy, and user experience.
Sincerely, MD. Nayem Mia, Bangladeshi, info.nayemislam@gmail.com

The issue identifies tokenization algorithms, training and fine-tuning pipelines, and system prompts or regional guardrails, but names no files, tests, or entry points. First determine which of these are owned by this repository, then define measurable Bengali tokenization and Bangladeshi Bengali representation criteria before implementation.