X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
This addresses the problem of inadequate multilingual agent development for global applications, though it's incremental as it extends existing benchmarking to new languages.
The authors tackled the lack of multilingual evaluation for LLM-based agents by introducing X-WebAgentBench, a benchmark that assesses planning and interaction across multiple languages, finding that even advanced models like GPT-4o with cross-lingual techniques perform poorly.
Recently, large language model (LLM)-based agents have achieved significant success in interactive environments, attracting significant academic and industrial attention. Despite these advancements, current research predominantly focuses on English scenarios. In reality, there are over 7,000 languages worldwide, all of which demand access to comparable agentic services. Nevertheless, the development of language agents remains inadequate for meeting the diverse requirements of multilingual agentic applications. To fill this gap, we introduce X-WebAgentBench, a novel multilingual agent benchmark in an interactive web environment, which evaluates the planning and interaction performance of language agents across multiple languages, thereby contributing to the advancement of global agent intelligence. Additionally, we assess the performance of various LLMs and cross-lingual alignment methods, examining their effectiveness in enhancing agents. Our findings reveal that even advanced models like GPT-4o, when combined with cross-lingual techniques, fail to achieve satisfactory results. We hope that X-WebAgentBench can serve as a valuable benchmark for multilingual agent scenario in real-world applications.