Diffusion Language Model for Accurate Corpus Processing
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Solution Overview
Problem
Autoregressive language models suffer from error accumulation and lack of global vision during iterative training, leading to poor quality and low accuracy in corpus processing results.
Innovation Solution
A target diffusion language model is trained using a diffusion model approach, where a mask language model is pre-trained and then fine-tuned using corpus samples with varying mask rates to improve training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autoregressive language model is used for corpus processing, then processing capability is provided, but accuracy and quality of results deteriorate due to error accumulation and lack of global vision
Solution Approach 1:
The patent changes the fundamental parameter of the language model architecture from autoregressive to diffusion-based. This parameter change enables the model to process corpus data with different mask rates, allowing it to capture global context and reduce error accumulation, thereby improving accuracy while maintaining processing capability
Solution Approach 2:
The patent applies preliminary action through pre-training the diffusion language model on corpus samples with various mask rates before actual processing. This pre-training phase equips the model with global vision and error correction capabilities, enabling it to handle complex corpus processing tasks with higher accuracy
2Measurement precision
If diffusion model approach is used with multiple mask rates, then accuracy and quality improve, but training complexity increases
Solution Approach 1:
The patent makes the training process universal by using corpus samples with multiple mask rates that can serve multiple purposes: teaching the model to handle different levels of data corruption, improving robustness, and enabling flexible processing of various corpus types. This multi-functionality justifies the increased training complexity by delivering broad performance improvements
Solution Approach 2:
The patent systematically varies the mask rate parameter during training to create diverse training scenarios. By changing this single parameter across multiple training iterations, the model learns to handle various corpus processing challenges, improving accuracy without requiring fundamentally different training approaches for each task type
Data Source
AI summary
The embodiment of the disclosure provides a method, apparatus, electronic device, and storage medium for data processing. The method includes: receiving a corpus to be processed; obtaining a target prediction result corresponding to the corpus to be processed by processing the corpus to be processed based on a target diffusion language model, wherein the target diffusion language model is obtained by training based on a plurality of corpus samples, and a mask corpora in the corpus samples corresponds to different mask rates; and displaying the target prediction result. According to the technical solution of the embodiment of the disclosure, the effect of making the target diffusion language model can process the corpus data based on the principle of diffusion model and the obtained corpus data processing results can meet the requirements of corpus processing tasks is implemented.