Summary of Mitigating the Language Mismatch and Repetition Issues in Llm-based Machine Translation Via Model Editing, by Weichuan Wang et al.
Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing
by Weichuan Wang, Zhaoyi Li, Defu Lian, Chen Ma, Linqi Song, Ying Wei
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 explores the potential of utilizing Large Language Models (LLMs) for machine translation, addressing two common error patterns: language mismatch and repetition. The authors investigate model editing methods to mitigate these issues by locating specific neurons responsible for errors and deactivating them during inference time. They find that directly applying these methods has limited effect on targeted errors or significant negative side-effects on general translation quality. To refine the located components, the authors propose fetching the intersection of results under different language settings and filtering out irrelevant information. The experimental results demonstrate that this approach can effectively reduce error ratios while maintaining general translation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make Large Language Models better at translating languages. Right now, these models are not perfect because they often get words or phrases mixed up or repeat themselves. The authors want to fix this by finding the parts of the model that cause these problems and turning them off when the model is making translations. They tried doing this, but it didn’t work very well. So, they came up with a new plan: find the common mistakes the model makes across different languages and use those to improve the translation quality. The results show that this approach can make the translations better while keeping the overall quality good. |
Keywords
» Artificial intelligence » Inference » Translation
