Multi-Turn Context Interpreter for Low-Token Conversation State
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Solution Overview
Problem
Existing context management assistants suffer from inaccuracies and inefficiencies in maintaining contextual understanding during multi-turn conversations, particularly due to retokenizing entire conversations at each turn, leading to excessive computational resource consumption.
Innovation Solution
A multi-turn interpreter system that utilizes a context engine and large language model to generate contextual states, pauses execution upon encountering interruption events, and resumes with supplemental data, reducing computational overhead by using context-aware prompts and code instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire conversation is retokenized at each turn to maintain contextual understanding, then contextual accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the conversation processing into two distinct parts: (1) tokenization of individual turns which is computationally efficient, and (2) maintenance of a separate conversation state object that stores contextual information. This segmentation allows the system to avoid retokenizing the entire conversation while still maintaining accurate context through the state object that is updated incrementally with each turn.
Solution Approach 2:
The patent performs preliminary tokenization and context extraction for each conversation turn before adding it to the state. The conversation state object is built incrementally with pre-processed contextual information including topic, entities, and key phrases extracted in advance. This preliminary action eliminates the need for expensive full-conversation retokenization at each subsequent turn.
2Measurement precision
If larger language models with more parameters are applied to improve contextual understanding, then accuracy is improved, but computational expense increases
Solution Approach 1:
The patent divides the contextual understanding task between a smaller language model and a dedicated conversation state management component. The language model only processes individual turns or short sequences, while the state object handles long-term context maintenance. This segmentation allows using a smaller, more efficient model while achieving the same overall contextual understanding through the combined system.
Solution Approach 2:
The conversation state object acts as an intermediary between the language model and the full conversation history. Instead of requiring the language model to directly process and remember entire conversation histories, the state object mediates by storing and managing contextual information, allowing the model to focus on processing current inputs with reduced computational burden.
3Measurement precision
If the entire conversation is retokenized at each turn to track context, then contextual precision is improved, but processing time increases
Solution Approach 1:
The patent segments conversation processing into efficient per-turn tokenization and incremental state updates. By maintaining a conversation state object that stores contextual information in a structured format with topic, entities, and key phrases, the system avoids the time-consuming full-conversation retokenization while preserving contextual precision through the segmented approach.
Solution Approach 2:
The patent ensures continuous accumulation of contextual information in the state object across turns without interruption or reprocessing. The state is updated incrementally with each turn, maintaining continuity of context while avoiding redundant processing. This continuous action preserves contextual precision while minimizing processing time by eliminating repeated full-tokenization cycles.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for maintaining context during multi-turn interactions. In one or more embodiments, the disclosed systems can detect an interruption event during the execution of computer code by an interpreter that pauses the execution of computer code by the interpreter. The disclosed systems can store, based on the interruption event, a serialized state of the interpreter that indicates a pause location in the computer code by encoding the code execution data. In some cases, the disclosed systems can resume execution of the computer code from the serialized state of the interpreter.