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New BANGLAPLAN benchmark tests LLM planning in Bangla

By PulseAugur Editorial · [1 sources] ·

A new benchmark called BANGLAPLAN has been developed to evaluate the constraint-based planning and decision-making capabilities of large language models in the Bangla language. The benchmark consists of 1,000 problems across ten task families, designed to reflect real-life scenarios in Bangladesh and covering various language registers and answer formats. Initial evaluations show a significant performance gap between leading models like GPT-6.1 Sol, which achieved 98.7% accuracy, and others, with many models scoring below a simple baseline. AI

IMPACT This benchmark will enable better evaluation of LLMs for complex reasoning and planning tasks in underrepresented languages like Bangla.

RANK_REASON The cluster describes a new academic benchmark for evaluating LLM capabilities in a specific language and task domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BANGLAPLAN benchmark tests LLM planning in Bangla

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The cluster describes a new academic benchmark for evaluating LLM capabilities in a specific language and task domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Md Rayhanul Masud, S M Maksudul Alam, Mohammad Jaminur Islam ·

    BANGLAPLAN: A Benchmark for Constraint-Based Planning and Decision-Making in Bangla

    arXiv:2610.19704v1 Announce Type: new Abstract: Large language models (LLMs) increasingly help users arrange appointments, plan journeys, and allocate limited resources. These tasks require models to satisfy constraints on time, cost, capacity, and order, or determine that no fea…

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