LLM Output Streaming With Segment Rendering for Lower Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models (LLMs) introduce additional latency in generating and rendering natural language (NL) output, prolonging user interactions and detracting from the user experience.
Innovation Solution
A system processes NL input using a LLM to generate a stream of NL output on a segment-by-segment basis, selecting and rendering each segment as it is generated, while continuously updating the LLM state to constrain the search space and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire NL output is generated and evaluated before rendering, then the accuracy and completeness of the output is improved, but the latency in rendering the output increases
Solution Approach 1:
The NL output is divided into multiple segments that can be processed and rendered incrementally. The system generates and renders segments one at a time rather than waiting for the complete output, reducing latency while maintaining overall accuracy through continuous evaluation.
Solution Approach 2:
The system performs preliminary rendering of output segments before the complete output is generated. By rendering segments as they become available and using stop sequences to control termination, the system reduces waiting time while ensuring the output meets accuracy requirements.
2Loss of time
If segments are rendered as they are generated, then the rendering latency is reduced, but the system complexity increases due to segment management and state updates
Solution Approach 1:
The system implements feedback mechanisms where the state is continuously updated based on rendered segments and stop sequences. This feedback loop allows the system to monitor progress and control the generation process dynamically, managing complexity through structured state management.
Solution Approach 2:
The system uses dynamic stop sequences that can be adjusted during the generation process. This allows flexible control over when rendering should stop, enabling the system to adapt to different requirements while managing the complexity of segment-by-segment processing.
3Productivity
If chunked decoding is used to generate segments, then the productivity is improved, but the loss of information increases due to incomplete context in partial segments
Solution Approach 1:
The system maintains continuous generation and rendering of segments without interruption. By processing segments in a continuous stream and updating the state progressively, the system preserves context information across segments while maintaining high generation speed.
Data Source
AI summary
Implementations relate to reducing latency in generating and/or rendering natural language (NL) output generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, and generate the NL based output utilizing the LLM. The NL based output can be a stream of NL based output in that it includes a plurality of segments, and is generated on a segment-by-segment basis. In some implementations, a first segment of the stream of NL based output is selected for inclusion in the stream of NL based output as a second segment (and any subsequent segment) is being generated to reduce latency in evaluating the NL based output as a whole prior to rendering thereof. In some versions of those implementations, the first segment is rendered as the second segment (and any subsequent segment) is being generated to further reduce latency in rendering thereof.