arXiv:2606.15532v1 Announce Type: new Abstract: Emotional intelligence (EI) in Large Language Models (LLMs) is often evaluated through static understanding tasks or single-response dialogue generation. However, emotion management is interactive: a good model should not only recognize a user's emotion, but also improve the user's emotional and relational state over several turns. We introduce EIBench, a simulator-based benchmark for interactive emotion management. EIBench contains 2,222 scenarios, with 2,009 for training and 213 for held-out testing. The scenarios are organized by a 2x2 taxonom
The increasing sophistication and widespread deployment of Large Language Models necessitate more robust and interactive evaluation benchmarks to push towards genuinely intelligent and emotionally aware AI.
This development addresses a critical limitation in current AI evaluation by moving beyond static tasks to interactive, multi-turn emotion management, essential for human-AI collaboration and agentic systems.
The introduction of EIBench shifts the methodology for assessing emotional intelligence in LLMs from simple recognition to complex, interactive management, potentially accelerating advancements in socially aware AI.
- · AI researchers in social intelligence
- · Developers of empathetic AI agents
- · Industries relying on human-chatbot interaction
- · LLMs with only static emotional understanding
- · Evaluation frameworks focused solely on single-turn responses
Further research and development will focus on interactive emotion management capabilities in LLMs.
Improved emotional intelligence in AI could lead to more effective and trusted AI agents in various applications.
Widely adopted emotionally intelligent AI might significantly alter human-computer interaction paradigms, fostering deeper engagement and reliance.
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Read at arXiv cs.CL