Dynamic Self-Adaptation Mechanism for Contextual Consistency Using Probabilistic Memory Synthesis
Year: 2024
Type: preprint
Abstract: The increasing complexity and length of user prompts have exposed limitations in existing Large Language Models (LLMs), particularly concerning their capacity to maintain contextual coherence over extended interactions. Addressing this challenge, the Dynamic Self-Adaptation Mechanism (DSAM) introduces an innovative approach by incorporating Probabilistic Memory Synthesis, enabling LLMs to dynamically adjust their contextual understanding in response to evolving inputs. Through empirical evaluation, the DSAM-enhanced LLM demonstrated significant improvements in response coherence, memory retention, and adaptability, thereby surpassing the performance of traditional static memory architectures. This advancement underscores the potential of adaptive memory mechanisms to enhance the contextual sensitivity and responsiveness of LLMs, marking a pivotal step forward in the development of more sophisticated language processing systems.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4404354727