Summary of Drt: Deep Reasoning Translation Via Long Chain-of-thought, by Jiaan Wang et al.
DRT: Deep Reasoning Translation via Long Chain-of-Thought
by Jiaan Wang, Fandong Meng, Yunlong Liang, Jie Zhou
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to neural machine translation (MT) that leverages the concept of long chain-of-thought (CoT) in reasoning tasks. Inspired by the success of O1-like models in math and coding tasks, the authors develop a multi-agent framework to translate sentences containing similes or metaphors from existing literature books. The framework iteratively translates the source sentence under the suggestions provided by an advisor, with an evaluator quantifying the translation quality at each round. This approach leads to the collection of tens of thousands of long-thought MT data, which is used to train the DRT models. Experiments using Qwen2.5 and LLama-3.1 as backbones show that the DRT models outperform vanilla LLMs and LLMs fine-tuning on paired sentences without long thought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make machine translation better by making machines think more like humans do when they translate tricky texts with similes and metaphors. The authors create a special system that asks for help from another “expert” translator, and then checks the quality of the translated text at each step. This helps gather lots of data on how machines can think more like humans during translation. They tested their approach using two different machine learning models and found it works better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Machine learning » Translation
