TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla

• URL: http://arxiv.org/abs/2509.09101v1
• Date: Thu, 11 Sep 2025 02:25:49 GMT
• Title: TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla
• Authors: Nishat Raihan, Antonios Anastasopoulos, Marcos Zampieri,
• Abstract summary: Despite being the 5th most spoken language, Bangla remains underrepresented in Large Language Models (LLMs)<n>This primarily stems from the scarcity of high-quality data to pre-train and/or finetune such models.<n>We offer three major contributions: (1) a comprehensive Bangla code instruction datasets for programming domain adaptation; (2) MBPP-Bangla, an evaluation benchmark for Bangla code generation; and (3) the TigerCoder-family of Code LLMs, achieving significant 11-18% performance gains at Pass@1 over existing multilingual and general-purpose Bangla LLMs.
• Score: 37.210208249613
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite being the 5th most spoken language, Bangla remains underrepresented in Large Language Models (LLMs), particularly for code generation. This primarily stems from the scarcity of high-quality data to pre-train and/or finetune such models. Hence, we introduce the first dedicated family of Code LLMs for Bangla (1B & 9B). We offer three major contributions: (1) a comprehensive Bangla code instruction datasets for programming domain adaptation; (2) MBPP-Bangla, an evaluation benchmark for Bangla code generation; and (3) the TigerCoder-family of Code LLMs, achieving significant ~11-18% performance gains at Pass@1 over existing multilingual and general-purpose Bangla LLMs. Our findings show that curated, high-quality datasets can overcome limitations of smaller models for low-resource languages. We open-source all resources to advance further Bangla LLM research.

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