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MemEmo: Evaluating Emotion in Memory Systems of Agents

arXiv:2602.23944v1 Announce Type: new Abstract: Memory systems address the challenge of context loss in Large Language Model during prolonged interactions. However, compared to human cognition, the efficacy of these systems in processing emotion-related information remains inconclusive. To address this gap, we propose an emotion-enhanced memory evaluation benchmark to assess the performance of mainstream and state-of-the-art memory systems in handling affective information. We developed the \textbf{H}uman-\textbf{L}ike \textbf{M}emory \textbf{E}motion (\textbf{HLME}) dataset, which evaluates memory systems across three dimensions: emotional information extraction, emotional memory updating, and emotional memory question answering. Experimental results indicate that none of the evaluated systems achieve robust performance across all three tasks. Our findings provide an objective perspective on the current deficiencies of memory systems in processing emotional memories and suggest a new

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Peng Liu, Zhen Tao, Jihao Zhao, Ding Chen, Yansong Zhang, Cuiping Li, Zhiyu Li, Hong Chen · · 1 min read · 12 views
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arXiv:2602.23944v1 Announce Type: new Abstract: Memory systems address the challenge of context loss in Large Language Model during prolonged interactions. However, compared to human cognition, the efficacy of these systems in processing emotion-related information remains inconclusive. To address this gap, we propose an emotion-enhanced memory evaluation benchmark to assess the performance of mainstream and state-of-the-art memory systems in handling affective information. We developed the \textbf{H}uman-\textbf{L}ike \textbf{M}emory \textbf{E}motion (\textbf{HLME}) dataset, which evaluates memory systems across three dimensions: emotional information extraction, emotional memory updating, and emotional memory question answering. Experimental results indicate that none of the evaluated systems achieve robust performance across all three tasks. Our findings provide an objective perspective on the current deficiencies of memory systems in processing emotional memories and suggest a new trajectory for future research and system optimization.

Executive Summary

This article proposes an emotion-enhanced memory evaluation benchmark, the Human-Like Memory Emotion (HLME) dataset, to assess the performance of mainstream and state-of-the-art memory systems in handling affective information. The HLME dataset evaluates memory systems across three dimensions: emotional information extraction, emotional memory updating, and emotional memory question answering. Experimental results indicate that none of the evaluated systems achieve robust performance across all three tasks. The findings suggest a new trajectory for future research and system optimization, highlighting the current deficiencies of memory systems in processing emotional memories.

Key Points

  • ▸ The HLME dataset provides a comprehensive evaluation framework for memory systems in processing affective information.
  • ▸ The experimental results demonstrate the need for improved emotional memory processing capabilities in mainstream and state-of-the-art memory systems.
  • ▸ The study's findings have significant implications for the development of more human-like and emotionally intelligent AI systems.

Merits

Comprehensive Evaluation Framework

The HLME dataset provides a structured and comprehensive evaluation framework for memory systems, enabling researchers to identify areas for improvement and optimize system performance.

In-Depth Analysis of Emotional Memory

The study's focus on emotional memory processing sheds light on the current limitations of mainstream and state-of-the-art memory systems, providing valuable insights for future research and development.

Demerits

Limited Generalizability

The study's findings may not be directly generalizable to other memory systems or applications, highlighting the need for further research to validate the HLME dataset's effectiveness across different contexts.

Scalability and Complexity

The HLME dataset's three-dimensional evaluation framework may introduce scalability and complexity challenges, requiring significant computational resources and expertise to develop and implement.

Expert Commentary

The HLME dataset proposed in this study represents a significant advancement in the evaluation of memory systems' emotional memory processing capabilities. The comprehensive evaluation framework and in-depth analysis of emotional memory provided by the HLME dataset offer valuable insights for future research and development. However, the study's limitations, such as limited generalizability and scalability, highlight the need for further research to validate the HLME dataset's effectiveness across different contexts. The implications of the study's findings are far-reaching, with significant implications for the development of more human-like and emotionally intelligent AI systems.

Recommendations

  • ✓ Future research should focus on developing and validating more comprehensive evaluation frameworks for memory systems, including the HLME dataset.
  • ✓ Developers of mainstream and state-of-the-art memory systems should prioritize the incorporation of emotional memory processing capabilities to improve the human-like and emotionally intelligent nature of their systems.

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