BANGLAPLAN: A Benchmark for Constraint-Based Planning and Decision-Making in Bangla
We introduce BANGLAPLAN, a benchmark of 1,000 problems across ten task families grounded in everyday life and institutions in Bangladesh.
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Key points
- Large language models (LLMs) increasingly help users arrange appointments, plan journeys, and allocate limited resources.
- However, benchmarks for evaluating planning and decision-making in Bangla under explicit constraints remain lacking.
- We construct the benchmark through language-model-assisted drafting and independent human validation.
- We find a large performance gap: GPT-6.1 Sol achieves 98.7% accuracy and Qwen3.5-27B with thinking reaches 80.3%, while direct-answer configurations score at most 30.9%.
Sources (1)
- [1]BANGLAPLAN: A Benchmark for Constraint-Based Planning and Decision-Making in BanglaarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:32 AM
We introduce BANGLAPLAN, a benchmark of 1,000 problems across ten task families grounded in everyday life and institutions in Bangladesh.
Large language models (LLMs) increasingly help users arrange appointments, plan journeys, and allocate limited resources.
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