TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla
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| Format: | Preprint |
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2025
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| _version_ | 1866911149253984256 |
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| author | Raihan, Nishat Anastasopoulos, Antonios Zampieri, Marcos |
| author_facet | Raihan, Nishat Anastasopoulos, Antonios Zampieri, Marcos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla Raihan, Nishat Anastasopoulos, Antonios Zampieri, Marcos Computation and Language 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. |
| title | TigerCoder: A Novel Suite of LLMs for Code Generation in Bangla |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.09101 |