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About this paper
Raghav Garg, Kapil Sharma, Karan Gupta
Researchers from Sprinklr developed CXMArena, a unified, large-scale synthetic benchmark designed to evaluate AI performance across five critical operational tasks in customer experience management. This benchmark addresses data scarcity and privacy concerns by utilizing an LLM-powered generation pipeline and reveals that current state-of-the-art models struggle significantly with these realistic scenarios.
Key insights
- State-of-the-art LLMs face considerable challenges in complex, realistic CXM tasks, often struggling with nuanced semantic understanding, robust grounding, and avoiding hallucination.
- Tool-calling precision in LLMs decreases notably as the number of available tools increases, indicating scalability challenges for function-calling in complex environments.
- The synthetic data generation pipeline, incorporating controlled noise and LLM-based validation, effectively bypasses privacy concerns while producing realistic and high-quality benchmark data.
Summary by alphaXiv.
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