[R] Enhancing Language Model Performance through Context Preservation: A Novel Approach Utilizing Internal State Symbols

Abstract

In the domain of conversational AI, the quality of output generated by large language models (LLMs) is of significant importance. This paper explores a novel approach to provide context and improve the quality of LLM responses in conversational settings. The proposed technique involves instructing the LLM to output a series of symbols representing its internal state at the end of its last response, which encapsulates the context and process that led to that answer. When provided with symbols from …

abstract context conversational conversational ai generated importance language language model language models large language models llm llms machinelearning novel paper performance preservation quality responses state through