Streaming LLM Input Processing for Real-Time Token Generation
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Solution Overview
Problem
Current large language models (LLMs) lack real-time capabilities, restricting their applicability in scenarios requiring continuous and immediate interaction with dynamic inputs, such as real-time transcription and interactive dialogues, due to their turn-based operation and complex architectures.
Innovation Solution
A machine learning model that processes incoming data in real-time by generating tokens sequentially, utilizing a generation loop after each received token, and incorporating BLANK symbols to control the output flow, enabling continuous engagement with dynamic inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs operate in a turn-based manner requiring complete prompt input before generating response, then the model can process information accurately, but real-time processing capability is lost
Solution Approach 1:
The patent segments the input stream into discrete chunks that can be processed incrementally. Instead of requiring the complete prompt to be available before processing, the model processes information in segments as they arrive, enabling real-time transcription and translation while maintaining accuracy through progressive refinement of predictions.
Solution Approach 2:
The system performs preliminary actions by generating provisional predictions based on partial input data before the complete information is available. These preliminary predictions are then refined as more data arrives, allowing the system to provide real-time outputs without sacrificing final accuracy.
2Speed
If complex architectures like RNN-T and AED models are used to achieve real-time processing, then real-time capability is improved, but system complexity increases
Solution Approach 1:
The patent applies a universal transformer-based architecture that can handle multiple tasks (transcription, translation, summarization) and operate in different modes (real-time streaming, batch processing) without requiring separate specialized models. This multi-functionality reduces overall system complexity while maintaining real-time capabilities.
Solution Approach 2:
The system achieves real-time processing by adjusting key parameters such as sequence length, attention window size, and processing batch sizes rather than fundamentally changing the architecture. These parameter modifications enable efficient real-time operation within the existing transformer framework without the complexity of specialized real-time architectures.
3Measurement precision
If the model waits for complete utterances before generating text, then transcription accuracy improves, but response time increases
Solution Approach 1:
The patent implements continuous useful action by processing and generating text throughout the entire duration of speech input rather than pausing until completion. The model continuously updates predictions as new audio data arrives, providing ongoing transcription output that improves over time while reducing the perceived response delay.
Solution Approach 2:
The system employs feedback mechanisms where previous predictions and intermediate results are fed back into the model to inform subsequent processing. This allows the model to maintain and refine transcription accuracy over time while providing continuous real-time output, effectively resolving the trade-off between accuracy and response time.
Data Source
AI summary
A system and method to train a large language model are provided. The system may access training data including one or more events and including one or more frames of a fixed duration. The system may further generate a label sequence based on the training data, and the system may determine an interleaved embedding sequence from the label sequence. The system may further determine a probability distribution over one or more predicted tokens based at least in part on an embedding of the interleaved embedding sequence. The system may further determine a difference between the probability distribution over the one or more predicted tokens and the label sequence. The system may further modify one or more parameters of the large language model based on the determined difference.