DRT-o1：透過長度連貫的思考鏈路進行優化的深度推理翻譯
DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought
摘要
Summary
AI-Generated Summary
Paper Overview
The paper introduces DRT-o1, applying long chain-of-thought reasoning to neural machine translation for handling literature texts with similes and metaphors. It demonstrates the effectiveness of DRT-o1 in improving translation quality compared to existing models, achieving significant BLEU and CometScore improvements.
Core Contribution
- Introduction of DRT-o1 for neural machine translation with long-thought reasoning.
- Development of a multi-agent framework for synthesizing long-thought machine translation samples.
- Validation of DRT-o1's effectiveness in literature translation tasks.
Research Context
The research addresses the need for enhanced translation models capable of handling complex literary texts with similes and metaphors, filling a gap in existing neural machine translation systems by incorporating long chain-of-thought reasoning.
Keywords
Long chain-of-thought reasoning, Neural Machine Translation, Literature Translation, Similes, Metaphors, Multi-Agent Framework
Background
The research background involves the challenge of translating literature texts with similes and metaphors using traditional neural machine translation systems. This study aims to bridge this gap by introducing a novel approach that leverages long chain-of-thought reasoning to enhance translation quality.
Research Gap
Existing literature lacks models specifically designed to handle the intricacies of literature translation with similes and metaphors, necessitating the development of specialized approaches like DRT-o1.
Technical Challenges
The technical obstacles include capturing the nuanced meanings of similes and metaphors in literary texts, ensuring fluency and readability in translations, and refining the long-thought reasoning process for effective neural machine translation.
Prior Approaches
Previous solutions in neural machine translation have not adequately addressed the unique challenges posed by literature translation, highlighting the need for innovative methodologies like DRT-o1.
Methodology
The methodology involves data collection from literature sources, the implementation of a multi-agent framework for iterative translation refinement, and the utilization of GPT-4o for long-thought reformulation in neural machine translation.
Theoretical Foundation
The theoretical basis lies in incorporating long chain-of-thought reasoning into neural machine translation to enhance the model's understanding of complex literary expressions like similes and metaphors.
Technical Architecture
The technical architecture includes the development of DRT-o1 models (DRT-o1-7B and DRT-o1-14B) using specific backbones (Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct) for instruct-tuning large language models (LLMs).
Implementation Details
Implementation involves using Llama-Factory for instruct-tuning LLMs, DeepSpeed optimization, ZeRO-3 optimization, and synthesizing long-thought machine translation samples through a translator, advisor, and evaluator in the multi-agent framework.
Innovation Points
The innovation lies in the integration of long chain-of-thought reasoning into neural machine translation, the development of DRT-o1 models tailored for literature translation, and the use of a multi-agent framework for refining translations iteratively.
Experimental Validation
The experimental validation includes conducting English-to-Chinese translation experiments, evaluating model performance using BLEU and CometScore metrics, and comparing DRT-o1 models with existing translation models to showcase their superior effectiveness.
Setup
Experiments involved synthesizing 22,264 machine translation samples with long thought using the multi-agent framework and specific model configurations (DRT-o1-7B and DRT-o1-14B).
Metrics
Evaluation metrics such as BLEU and CometScore were used to quantify the improvements in translation quality achieved by DRT-o1 models compared to baseline models.
Results
Results demonstrated the significant performance enhancements of DRT-o1 models in literature translation tasks, showcasing improvements in BLEU scores and CometScore metrics compared to existing models like QwQ-32B-Preview.
Comparative Analysis
Comparisons with Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct, QwQ-32B-Preview, and Marco-o1-7B models highlighted the superior translation quality and effectiveness of DRT-o1 models in handling literature texts with similes and metaphors.
Impact and Implications
The study's findings contribute to advancing neural machine translation models by showcasing the effectiveness of long chain-of-thought reasoning in enhancing translation quality for literature texts. The implications include potential applications in other reasoning tasks and the development of more sophisticated translation systems.
Key Findings
- Demonstrated effectiveness of DRT-o1 in improving translation quality for literature texts.
- Outperformed existing models in BLEU and CometScore metrics.
- Showcased the potential of long chain-of-thought reasoning in neural machine translation.
Limitations
- The study focused on English-to-Chinese translation, limiting generalizability to other language pairs.
- The effectiveness of DRT-o1 may vary depending on the complexity and style of the literature being translated.
Future Directions
- Explore the application of long chain-of-thought reasoning in other language pairs and translation domains.
- Investigate the scalability of the multi-agent framework to handle larger datasets and more diverse literary texts.
Practical Significance
- The findings have practical implications for improving the translation quality of literary works, enhancing cross-cultural communication, and advancing the capabilities of neural machine translation systems.