arXiv:2605.25680v1 Announce Type: new Abstract: Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap, we run a series of classic memory experiments from psychology on both humans and language models. Across tasks, we find that out-of-the-box language models exhibit better memory than humans, even when prompted to imitate human behavior. We then show that better prompting strategies and the use of a compactor can cause language models to forget content in a more human-like way. Using these methods,
This research is emerging as language models become ubiquitous and foundational to more complex AI systems, demanding more nuanced and human-like capabilities.
The ability to simulate human cognitive imperfections like forgetting is crucial for creating more realistic and trustworthy AI agents that interact seamlessly with human users.
AI models can now be engineered to exhibit more human-like memory characteristics, moving beyond perfect recall to incorporate planned 'forgetting' mechanisms for better user simulation and interaction.
- · AI agents developers
- · Psychological research in AI
- · Human-computer interaction designers
- · Overly simple AI simulation models
Language models can more accurately mimic human memory limitations for user simulation tasks.
This improved realism could lead to more effective and trustworthy AI assistants in empathetic roles.
The conscious engineering of 'forgetting' in AI might open new avenues for privacy-preserving AI or AI that adapts to information decay.
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