BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali
Learn to evaluate hallucination in large language models for Bengali using BenHalluEval, a multi-task framework that assesses generative question answering, code-mixed QA, summarization, and reasoning.
- Build a dataset of hallucinated candidates using GPT-5.4
- Configure the BenHalluEval framework for four tasks: GQA, Bangla-English Code-Mixed QA, Summarization, and Reasoning
- Run the evaluation framework on the constructed dataset
- Test the performance of large language models on Bengali using BenHalluEval
- Apply the results to fine-tune and improve model accuracy
NLP engineers and researchers working with large language models can benefit from BenHalluEval to systematically evaluate hallucination in Bengali, ensuring more accurate and reliable model performance.
💡 BenHalluEval provides a fine-grained evaluation of hallucination in large language models for Bengali, covering four tasks and enabling more accurate model performance.
🚀 Introducing BenHalluEval: a multi-task hallucination evaluation framework for large language models on Bengali! 🤖
Key Takeaways
Learn to evaluate hallucination in large language models for Bengali using BenHalluEval, a multi-task framework that assesses generative question answering, code-mixed QA, summarization, and reasoning.
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