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New LoWAgent benchmark reveals performance gaps in AI web agents for low-resource languages

By PulseAugur Editorial · [1 sources] ·

A new benchmark called LoWAgent has been introduced to evaluate the performance of web agents across low-resource languages, addressing a gap in current testing methodologies. The benchmark includes six localized websites and 3,000 task instances across English, Hindi, Bengali, Urdu, and Amharic. Initial evaluations revealed that both closed-source and open-source models exhibit performance degradation in low-resource languages compared to English, with open-source models showing a more significant drop. The study also found that interface disruptions like cookie notices further degrade agent performance, but post-training on LoWAgent data can substantially improve performance in these languages. AI

IMPACT Highlights the need for improved AI agent capabilities in multilingual contexts and suggests a path for enhancing performance in low-resource languages.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New LoWAgent benchmark reveals performance gaps in AI web agents for low-resource languages

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The cluster describes a new academic paper introducing a benchmark for evaluating AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ocean Monjur, Daud Ahmad Nisar, Shahriar Kabir Nahin, Tadesse Destaw Belay, Sushruti Mishra, Israel Abebe Azime, Anshuman Chhabra ·

    LoWAgent: A Low-Resource Multilingual Benchmark for Web Agents

    arXiv:2610.27205v1 Announce Type: cross Abstract: Web agents are typically evaluated on English-language interfaces, leaving their ability to operate across the world's multilingual websites largely unexplored. This gap is especially significant for low-resource languages, where …

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