OutboundEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Outbound Evaluation of Xbench's Professional-Aligned Series

2025-10-24

AI Summary

Existing LLM evaluation methods suffer from three critical limitations in expert-level intelligent outbound call scenarios: narrow data coverage, unrealistic user simulation, and片面 evaluation metrics. This paper introduces the first domain-specific benchmark for this setting, spanning six industries and thirty sub-scenarios. We propose a structured process-decomposition modeling framework and a dual-dimensional, domain-adaptive evaluation system—assessing both task completion and interaction quality—augmented by a domain-aligned weighted scoring mechanism and a dynamic human-AI collaborative evaluation framework. Innovatively, we develop an LLM-driven virtual user simulator endowed with personality traits and emotion evolution, enabling high-fidelity interaction modeling. Empirical evaluation across twelve mainstream LLMs reveals a significant trade-off between task completion and interaction fluency. This work establishes a reproducible, scalable, and standardized evaluation paradigm—with empirical grounding—for professional outbound-call AI systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

We propose OutboundEval, a comprehensive benchmark for evaluating large language models (LLMs) in expert-level intelligent outbound calling scenarios. Unlike existing methods that suffer from three key limitations - insufficient dataset diversity and category coverage, unrealistic user simulation, and inaccurate evaluation metrics - OutboundEval addresses these issues through a structured framework. First, we design a benchmark spanning six major business domains and 30 representative sub-scenarios, each with scenario-specific process decomposition, weighted scoring, and domain-adaptive metrics. Second, we develop a large-model-driven User Simulator that generates diverse, persona-rich virtual users with realistic behaviors, emotional variability, and communication styles, providing a controlled yet authentic testing environment. Third, we introduce a dynamic evaluation method that adapts to task variations, integrating automated and human-in-the-loop assessment to measure task execution accuracy, professional knowledge application, adaptability, and user experience quality. Experiments on 12 state-of-the-art LLMs reveal distinct trade-offs between expert-level task completion and interaction fluency, offering practical insights for building reliable, human-like outbound AI systems. OutboundEval establishes a practical, extensible, and domain-oriented standard for benchmarking LLMs in professional applications.

Problem

• Evaluates LLMs in expert-level outbound calling scenarios
• Addresses limitations in dataset diversity and user simulation
• Provides dynamic evaluation with automated and human assessment

Innovation

• Multi-domain benchmark with scenario-specific metrics
• Large-model-driven simulator for realistic user interactions
• Dynamic evaluation combining automated and human assessment