Core Concepts
Efficiently decoding transformer models by encoding once and decoding in parallel improves efficiency and performance on structured output tasks.
Abstract
The content discusses a new configuration for encoder-decoder models called prompt-in-decoder (PID) that encodes input once and decodes output in parallel, reducing memory footprint. It highlights the benefits of this method on various tasks like dialogue state tracking, summarization, and question-answering. The study compares PID with other models, showcasing its computational reduction and speed-up advantages.
Abstract
Transformer-based NLP models have high computational costs.
Finetuned encoder-decoder models outperform larger decoder-only models.
Introducing prompt-in-decoder (PID) configuration for efficient decoding.
Introduction
Researchers explore model compression, architecture modifications, speculative decoding, and GPU optimizations to reduce computation costs.
Encoder-Decoder Framework
General framework overview using transformers for NLP tasks.
Multi-Prompt Decoding
Tasks framed with multiple prompts over the same input X.
Encode Once and Decode in Parallel
Proposal of PID method for efficient decoding strategy.
Performance Analysis
Operational intensity calculations for memory access and arithmetic operations.
Datasets & Metrics
Description of datasets used for dialogue state tracking, summarization, and question answering tasks.
Experiments & Results
Comparison of task performance between T5, PIE-T5, PID-T5 models.
Related Work
Previous studies on reducing model size, attention overheads, and parallel decoding methods.
Stats
We achieve computation reduction that roughly scales with the number of subtasks, gaining up to 4.6x speed-up over state-of-the-art models.
Our models achieve comparable or higher performance (98-101%) than the current state of the art.
Quotes
"Our method is compatible with efficiency techniques leading to further gains when used together."
"Subtasking approach allows addressing components individually leading to improved task performance."