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This paper focuses on the parallel processing potential of autoregressive decoding to address the inference latency issue of large-scale language models (LLMs). We propose an adaptive serial-parallel decoding (ASPD) technique that exploits intrinsic parallelism in the output of autoregressive models to perform parallel decoding. ASPD consists of a pipeline that automatically extracts and validates parallelizable data structures and a hybrid decoding engine that enables seamless switching between serial and parallel decoding modes. Experimental results on various tasks (general tasks, augmented search generation, and mathematical inference) demonstrate that ASPD outperforms existing methods in terms of efficiency and effectiveness, achieving an average speedup of 1.85x (up to 3.19x) on the Vicuna Bench while maintaining response quality degradation of less than 1%.
Takeaways, Limitations
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Takeaways:
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We present a novel parallel decoding technique that dramatically improves the inference speed of LLM.
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Substantial performance improvements through automated parallel structure extraction and efficient parallel decoding mechanisms.
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Expanding LLM deployment possibilities for latency-sensitive applications such as AI-powered customer service bots and answer search engines.
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Validation of effectiveness and efficiency through Vicuna Bench experiment results.
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Limitations:
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Further research is needed to evaluate the generalization performance of the proposed ASPD technique and its applicability to various LLM architectures.
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Ongoing research is needed to improve the accuracy and efficiency of automatic extraction of parallel-processable structures.
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Results are based on a specific benchmark (Vicuna Bench), and performance verification in other benchmarks or real-world application environments is required.
