Summary of Language Models and Cycle Consistency For Self-reflective Machine Translation, by Jianqiao Wangni
Language Models and Cycle Consistency for Self-Reflective Machine Translation
by Jianqiao Wangni
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Machine Learning (stat.ML)
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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 framework that utilizes large language models (LLMs) for machine translation. It starts with the conjecture that an ideal translation should contain complete and accurate information for a strong enough LLM to recover the original sentence. The authors generate multiple translation candidates from a source language A to a target language B, and then translate these candidates back to the original language A. They evaluate the cycle consistency between the original and back-translated sentences using metrics such as token-level precision and accuracy, which implicitly estimates the translation quality in language B without knowing its ground-truth. This approach also helps to evaluate the LLM’s translation capability using only monolingual corpora. The authors identify the translation candidate with optimal cycle consistency with the original sentence as the final answer. Their experiments demonstrate that larger LLMs or the same LLM with more forward passes during inference exhibit increased cycle consistency, aligning with the LLM model size scaling law and test-time computation scaling law. The paper provides methods for implicitly evaluating translation quality of a sentence in the target language, evaluating the capability of LLM for any-to-any-language translation, and generating better translations for specific LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to use large language models (LLMs) to translate text from one language to another. It’s like having a super-smart translator that can figure out what you want to say in another language. The researchers start by making many different versions of the original sentence, and then they use the LLMs to translate each version back into the original language. By comparing these translations to the original sentence, they can see how well the LLM is doing at translating text. They found that bigger LLMs are better at translating text, which makes sense because bigger computers are usually better at doing complex tasks. The paper shows us three ways that we can use this new approach: first, it helps us figure out if a translation is good or not; second, it lets us see how well an LLM is at translating text in general; and third, it gives us a way to make better translations for specific LLMs. |
Keywords
» Artificial intelligence » Inference » Precision » Token » Translation
