Alternative Frameworks and Benchmarks
Recent literature proposes alternative frameworks and benchmarks for evaluating emotional intelligence in AI. Here we clarify how the SERA-X model effectively integrates and surpasses these competing frameworks by providing a holistic approach.
Comparison Overview
| Framework | Focus | Limitations |
|---|---|---|
| Empathetic Chatbots (Agarwal et al., 2021) | Focused primarily on chatbot empathy using retrieval-based and generative-based evaluation. | Limited in scope to chatbot interactions. |
| Evaluating Empathy (Yalçın, 2019) | Provides separate system-level and feature-level evaluations. | Offers detailed analysis but lacks an integrative emotional-intelligence perspective across varied AI contexts. |
| Perceived Empathy (Concannon & Tomalin, 2023) | Uses Likert scale for subjective assessments. | Good at measuring perceived empathy but doesn't capture adaptive or context-sensitive emotional intelligence. |
| Foundation Metrics (Abbasian et al., 2024) | Suggests multi-dimensional evaluation for agent effectiveness. | Comprehensive but less nuanced in distinguishing discrete emotional and cognitive interactions. |
| EmOBench (Sabour et al., 2024) | Benchmarks LLM emotion recognition compared to human performance. | Narrowly specialized on LLM capabilities without broader applicability to diverse AI systems. |
Why SERA-X Stands Out
In contrast, the SERA-X framework uniquely combines various relevant emotional competencies— sensing, responding, adapting, and an extended perspective—into a coherent model. It supports comprehensive validation methodologies across diverse AI implementations (chatbots, agents, robots, and LLMs), effectively bridging specific emotional intelligence dimensions identified individually by these frameworks.