India

Detecting hate speech in Bangla is challenging due to its complex vocabulary, spelling variations, and region-specific word usage. However, effective detection is essential to ensure safer social media spaces and to take appropriate action against perpetrators. In this study, we report our participation in Subtask A of Task 1: Bangla Hate Speech Detection (Hasan et al., 2025b). In addition to the provided 50K Bangla comments (Hasan et al., 2025a), we collected approximately 4K Bangla comments and employed several data augmentation techniques. We evaluated several transformer-based models (e.g., BanglaBERT, BanglaT5, BanglaHateBERT), achieving the best performance with a micro-F1 score of 71% and securing 18th place in the Evaluation Phase.

IJCNLP-AACL 2025

CUET_Sntx_Srfrs at BLP-2025 Task 1: Combining Hierarchical Classification and Ensemble Learning for Bengali Hate Speech Detection

ensemble

hierarchical

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

workshop paper

Detecting hate speech in Bengali social media content presents considerable challenges, primarily due to the prevalence of informal language and the limited availability of annotated datasets. This study investigates the identification of hate speech in Bengali YouTube comments, focusing on classifying the type, severity, and target group. Multiple machine learning baselines and voting ensemble techniques are evaluated to address these tasks. The methodology involves text preprocessing, feature extraction using TF-IDF and Count vectors, and aggregating predictions from several models. Hierarchical classification with TF-IDF features and majority voting improves the detection of less frequent hate speech categories while maintaining robust overall performance, resulting in an $18^{th}$ place ranking and a micro F1 score of 68.42\%. Furthermore, ablation studies assess the impact of preprocessing steps and n-gram selection, providing reproducible baselines for Bengali hate speech detection. All codes and resources are publicly available at https://github.com/Hasan-Mesbaul-Ali-Taher/BLP_25_Task_1

Gradient Masters at BLP-2025 Task 1: Advancing Low-Resource NLP for Bengali using Ensemble-Based Adversarial Training for Hate Speech Detection

In this paper, we propose a system for generating Python code from Bangla prompts. Our approach fine-tunes open-source models with parameter-efficient techniques and leverages proprietary models via prompting. To enhance the reasoning of smaller models, we adopt a Chain-of-Thought (CoT) augmented fine-tuning, enabling them to learn intermediate reasoning steps before generating code. A self-refinement loop further improves performance by iteratively critiquing and correcting code based on execution feedback. We also employ few-shot prompting to guide inference more effectively. Applied to both open-source and proprietary models, this pipeline achieved its best results with Gemini 2.5 Pro, where our system ranked 4th on the competition leaderboard with a Pass@1 score of 0.85. We conclude with a detailed analysis of these findings.

NALA_MAINZ at BLP-2025 Task 2: A Multi-agent Approach for Bengali Instruction to Python Code Generation

This paper presents JGU Mainz’s winning system for the BLP-2025 Shared Task on Code Generation from Bangla Instructions. We propose a multi-agent-based pipeline. First, a code-generation agent produces an initial solution from the input instruction. The candidate program is then executed against the provided unit tests (pytest-style, assert-based). Only the failing cases are forwarded to a debugger agent, which reruns the tests, extracts error traces, and, conditioning on the error messages, the current program, and the relevant test cases, generates a revised solution. Using this approach, our submission achieved first place in the shared task with a Pass@1 score of 95.4. We also make our code public.

CUET_Expelliarmus at BLP2025 Task 2: Leveraging Instruction Translation and Refinement for Bangla-to-Python Code Generation with Open-Source LLMs

Large language models (LLMs) have recently shown strong performance in generating code from natural language prompts. However, current benchmarks are primarily focused on English overlooking low-resource languages like Bangla. This creates a critical research gap since there are no well established resources or systematic evaluations for code generation from Bangla instruction. To address the gap, we present a system that generates executable Python code from Bangla instructions. We design a two-stage pipeline where the Bangla instructions are first translated and refined into clear English version to reduce ambiguity and then the python code is generated from the refined instructions with iterative error-correction. For both instruction refinement and code generation we used the open-source GPT-20B OSS model. On the official test set our system achieves competitive results. We also analyze common errors like unclear instruction, logical mistakes, runtime issues and the need for external knowledge beyond the model’s training. Overall, our findings show that a simple translation–refinement pipeline can be an effective and low-cost approach for code generation in low-resource languages.

PyBhasha at BLP-2025 Task 2: Effectiveness of Semantic-Aware Translation and Ensembling in Bangla Code Generation

In this paper, we present our submission to Task 2 of the BLP-2025 shared task on code generation from Bangla instructions. Our approach focused on enhancing instruction quality through translation and improving model performance with a two-stage ensemble strategy. We evaluated two proprietary and several open-source models under three instruction settings: original Bangla instructions, Bangla instructions translated into English using Facebook NLLB, and instructions rewritten in English with GPT-4.1. Experimental results showed that GPT-4.1-rewritten instructions consistently achieved the highest accuracy across models. For final predictions, we used a two-stage ensemble, achieving a $\textit{pass@1}$ score of $\textbf{80.0\%}$ on the hidden test set and securing 12th place on the official leaderboard. Additionally, we conducted a qualitative analysis of selected translations to illustrate how variations in instruction phrasing influenced model outputs.

Code_Gen at BLP-2025 Task 2: BanglaCode: A Cross-lingual Benchmark for Code Generation with Translation and Assertion Strategies

Large Language Models (LLMs) have shown great code-generation capabilities, but their performance in low-resource languages like Bangla is largely unexplored. We participated in BLP-2025 Task 2: Code Generation in Bangla, where we built a pipeline to interpret and execute Bangla instructions using GPT-5. Extensive experiments were conducted with proprietary (GPT-4o Mini, GPT-5 Mini, GPT-5) and open-source (LLaMA 3-8B, TigerLLM-1B-it) models under translation and assertion settings. Results show that GPT-5, with translation and assertion, scored 83.8%, outperformed all baselines, while open-source models lagged due to limited Bangla adaptation. Assertion-based prompting always improved syntactic correctness, and fine-tuning reduced hallucinations across open-source models. We ranked 7th on the official leaderboard with an approach which is competitive and generalizable. Overall, our results show that translation quality, data normalization, and prompt design are key components of low-resource code generation. Furthermore, the proposed BanglaCode benchmark and preprocessing architecture provide a basis for further multilingual code-generation research.

Barrier Breakers at BLP-2025 Task 2: Enhancing LLM Code Generation Capabilities through Test-Driven Development and Code Interpreter

Over the past few years, improving LLM code generation capabilities has been a key focus in NLP research. Despite Bengali having 242 million native speakers worldwide, it receives little attention when it comes to training LLMs. More recently, various fine-tuning and augmented generation techniques have been employed to significantly enhance code generation performance. However, they require considerable expertise and resources to utilize effectively as an end user. The goal of our work is to democratize access to powerful code generation tools in resource-constrained emerging markets, enabling users to leverage them in their native language.

We introduce a novel approach that combines Test-Driven Development (TDD) and Code Interpreter (CI), utilizing open-weight models, which improves the baseline accuracy for code generation with Bengali prompts and achieves an overall accuracy of 85%. Our approach requires no finetuning and proves that even the smallest models in the same family can attain up to 98% accuracy compared to the largest models. All of our results are publicly shared in GitHub for validation and reproducibility.

Retriv at BLP-2025 Task 2: Test-Driven Feedback-Guided Framework for Bangla-to-Python Code Generation

Large Language Models (LLMs) have advanced the automated generation of code from natural language prompts. However, low-resource languages (LRLs) like Bangla remain underrepresented due to the limited availability of instruction-to-code datasets and evaluation benchmarks. To address this, the BLP Workshop at IJCNLP-AACL 2025 introduced a shared task on "Code Generation in Bangla". In this work, we propose a method that combines instruction prompting with a test-driven, feedback-guided iterative refinement process using a fine-tuned Qwen2.5-14B model. The model generates code from Bangla instructions, tests it against unit tests, and iteratively refines any failing outputs through three evaluation passes, using test feedback to guide each step. This approach helped our team "Retriv" to secure 2nd place in the shared task with a Pass@1 score of 0.934. The analysis highlights challenges in Bangla instruction understanding and Python code generation, emphasizing the need for targeted methods in LRLs. We made experimental scripts publicly available for the community.

TeamB2B at BLP-2025 Task 2: BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation

Bangla is a low-resource language for code generation, lacking large-scale annotated datasets and tools to transform natural language specifications into executable programs. This makes Bangla-to-code generation a challenging task requiring innovative solutions. To address this, we introduce BanglaForge, a novel framework for generating code from Bangla function descriptions. BanglaForge leverages a retrieval-augmented dual-model collaboration paradigm with self-refinement, combining in-context learning, llm-based translation, systematic prompt engineering, and iterative self-refinement based on execution feedback, where a coder generates initial solutions and a reviewer enhances them for robustness. On the BLP-2025 Bangla Code Generation benchmark, BanglaForge achieves a competitive Pass@1 accuracy of 84.00%, demonstrating the effectiveness of retrieval, model collaboration, and self-refinement for low-resource Bangla code generation.

Musafir at BLP_2025 Task 2: Generating Python Code from Bangla Prompts using a Multi Model Cascade and Unit Test Validation

This paper presents our approach for the BLP25 Task 2: Code Generation in Bangla. To address the scarcity of Bangla–code training data, we adopt a two-stage pipeline. First, Bangla problem statements are translated into English using a neural translation model optimized for preserving technical semantics. Then, the translated text is passed to a Qwen-based code generation model to produce executable solutions. This translation–generation strategy leverages the strengths of English-centric code models while ensuring fidelity to the original Bangla instructions. Our system achieved competitive performance on the leaderboard, achieving the 3rd place with score of 91.8% while demonstrating that translation-augmented pipelines are effective for low-resource code generation tasks.

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