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Unveiling Schrödinger’s Memory: Dynamic Memory Mechanisms in Transformer-Based Language Models

Unveiling Schrödinger’s Memory: Dynamic Memory Mechanisms in Transformer-Based Language Models

Practical Solutions and Value of Unveiling Schrödinger’s Memory in Language Models

Understanding LLM Memory Mechanisms

LLMs derive memory from input, not external storage, enhancing retention by extending context length and using external memory systems.

Exploring Schrödinger’s Memory

Hong Kong Polytechnic University researchers introduce “Schrödinger’s memory” in LLMs, dynamically approximating past information based on input cues.

Deep Learning Basis in Transformers

The Universal Approximation Theorem (UAT) forms the basis of memory in Transformer-based LLMs, allowing dynamic adjustment for memory-like capabilities.

Memory Capabilities of LLMs

LLMs exhibit memory by fitting input to output, similar to human recall, with larger models showing better performance and a correlation between input length and memory accuracy.

Comparing Human and LLM Cognition

LLMs and human cognition share dynamic reasoning abilities, adjusting to inputs for creativity and adaptability, with limitations attributed to model size, data quality, and architecture.

Conclusion: LLMs and Human Memory

LLMs demonstrate memory capabilities akin to human cognition, with “Schrödinger’s memory” revealed by specific inputs, validating LLM memory through experiments and exploring cognitive process similarities and differences.

For more details, check out the full research paper.

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Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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