MemBench: Towards Real-world Evaluation of Memory-Augmented Dialogue Systems
<|reference_start|>MemBench: Towards Real-world Evaluation of Memory-Augmented Dialogue Systems: Long-term memory is so important for chatbots and dialogue systems (DS) that researchers have developed numerous memory-augmented DS. However, their evaluation methods are different from the real situation in human conversation. They only measured the accuracy of factual information or the perplexity of generated responses given a query, which hardly reflected their performance. Moreover, they only consider passive memory retrieval based on similarity, neglecting diverse memory-recalling paradigms in humans, e.g. emotions and surroundings. To bridge the gap, we construct a novel benchmark covering various memory recalling paradigms based on cognitive science and psychology theory. The Memory Benchmark (MemBench) contains two tasks according to the two-phrase theory in cognitive science: memory retrieval, memory recognition and injection. The benchmark considers both passive and proactive memory recalling based on meta information for the first time. In addition, novel scoring aspects are proposed to comprehensively measure the generated responses. Results from the strongest embedding models and LLMs on MemBench show that there is plenty of room for improvement in existing dialogue systems. Extensive experiments also reveal the correlation between memory injection and emotion supporting (ES) skillfulness, and intimacy. Our code and dataset will be released.<|reference_end|>
@article{he2024madial-bench:,
    title={MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue
  Generation},
    author={Junqing He, Liang Zhu, Rui Wang, Xi Wang, Reza Haffari, Jiaxing Zhang},
    journal={arXiv preprint arXiv:2409.15240},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15240},
    primaryClass={cs.CL cs.AI}
}