NeKo: Naar Postherkenningsgeneratiecorrectie met Grote Taalmodellen met Taakgerichte Experts

NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts

November 8, 2024
Auteurs: Yen-Ting Lin, Chao-Han Huck Yang, Zhehuai Chen, Piotr Zelasko, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Szu-Wei Fu, Ke Hu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang
cs.AI

Samenvatting

Het opzetten van een algemene foutcorrector na herkenning stelt een cruciale vraag: hoe kunnen we op de meest effectieve manier een model trainen op een grote mix van domeindatasets? Het antwoord zou liggen in het leren van dataset-specifieke kenmerken en het verwerken van hun kennis in een enkel model. Eerdere methoden bereiken dit door aparte correctietaalmodellen te hebben, wat resulteert in een aanzienlijke toename van parameters. In dit werk presenteren we Mixture-of-Experts als een oplossing, waarbij we benadrukken dat MoE's veel meer zijn dan een schaalbaarheidstool. We stellen een Multi-Task Correction MoE voor, waarbij we de experts trainen om een "expert" te worden in spraak-naar-tekst, taal-naar-tekst en visie-naar-tekst datasets door te leren om de tokens van elke dataset naar de bijbehorende expert te routeren. Experimenten op de Open ASR Leaderboard tonen aan dat we een nieuwe state-of-the-art prestatie verkennen door een gemiddelde relatieve 5,0% WER-vermindering te behalen en aanzienlijke verbeteringen in BLEU-scores voor spraak- en vertaaltaken. Bij zero-shot evaluatie presteert NeKo beter dan GPT-3.5 en Claude-Opus met een relatieve WER-vermindering van 15,5% tot 27,6% in de Hyporadise benchmark. NeKo presteert competitief op grammatica- en post-OCR-correctie als een multi-task model.
English
Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset's tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-Opus with 15.5% to 27.6% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.

Summary

AI-Generated Summary

Paper Overview

This paper introduces the NEKO model, a Multi-Task Correction Mixture-of-Experts (MoE) model, showcasing significant improvements in word error rate (WER) reduction and BLEU scores for various tasks. NEKO outperforms existing models like GPT-3.5 and Claude-Opus, demonstrating state-of-the-art performance in error correction tasks.

Core Contribution

The key innovation lies in the utilization of a Multi-Task Correction MoE model, training experts for speech-to-text, language-to-text, and vision-to-text datasets, resulting in enhanced performance across multiple domains.

Research Context

The research addresses the need for an effective post-recognition error corrector trained on diverse domain data, surpassing previous methods with separate correction models by employing a unified MoE approach.

Keywords

  • Multi-Task Correction MoE model
  • Word Error Rate (WER) reduction
  • BLEU scores
  • Mixture-of-Experts (MoE)
  • Error correction tasks

Background

The study focuses on developing a comprehensive post-recognition error corrector by training on a diverse mix of domain data, aiming to overcome the limitations of previous methods with separate correction models.

Research Gap

Existing literature lacked a unified approach for error correction across domains, leading to increased parameters and reduced efficiency.

Technical Challenges

Challenges included minimizing negative log-likelihood, training on multiple error correction datasets, and ensuring task-specific expert allocation.

Prior Approaches

Previous methods relied on individual correction models, resulting in parameter inflation and reduced effectiveness, highlighting the need for a more integrated approach.

Methodology

The methodology involves training the NEKO model on various error correction datasets, utilizing a task-specific expert assignment within a Multi-Task Correction MoE framework to achieve superior performance.

Theoretical Foundation

NEKO is based on the Transformer architecture, employing dense and MoE models for fine-tuning, aiming to minimize negative log-likelihood for target sequences.

Technical Architecture

The NEKO model leverages a Multi-Task Correction MoE setup, training experts for specific domains to capture task-specific features and enhance performance.

Implementation Details

NEKO is implemented using a MoE approach for error correction tasks, demonstrating improved results compared to baseline models across benchmarks and OCR error correction.

Innovation Points

NEKO's innovation lies in its use of MoE to capture task-specific features effectively, leading to superior performance in error correction tasks.

Experimental Validation

The experimental validation involves training and evaluating NEKO on various datasets for ASR, ST, MT, OCR, and TEC tasks, showcasing its state-of-the-art performance in error correction and translation tasks.

Setup

Exact configurations and parameters for training NEKO on diverse error correction datasets, leading to significant improvements in WER reduction and BLEU scores.

Metrics

Evaluation criteria include WER reduction, BLEU scores, and comparative analysis with baseline models like GPT-3.5 and Claude-Opus, demonstrating NEKO's competitive performance.

Results

Quantitative and qualitative findings show NEKO's superiority in error correction tasks, outperforming existing models and achieving state-of-the-art performance in various benchmarks.

Comparative Analysis

NEKO is compared against baseline models and demonstrates superior performance in OCR error correction, grammar correction, and translation tasks, showcasing its effectiveness across different domains.

Impact and Implications

The NEKO model's impact is significant, offering improved error correction capabilities across diverse domains like healthcare, education, and customer service, with implications for future research and practical applications.

Key Findings

NEKO achieves state-of-the-art WER reduction, outperforming existing models in error correction tasks and demonstrating competitive performance in OCR and translation tasks.

Limitations

Challenges include dataset diversity, assumptions in error distribution, and potential overfitting with task-specific fine-tuning, necessitating further research for enhanced adaptability.

Future Directions

Future research opportunities include exploring advanced expert assignment strategies, enhancing interpretability of expert representations, and optimizing training processes for sustainable AI development practices.

Practical Significance

NEKO's application of MoE for error correction tasks offers practical benefits in improving the accuracy of automated systems, with potential for broader applications in real-world scenarios.

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