mlsanityData to 14 Oct 2026

PMMT: Preference Alignment in Multilingual Machine Translation via LLM Distillation

arXiv 2410.11410 ↗First posted 15 Oct 2024cs.CL cs.AIcited by 0

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Translation is important for cross-language communication, and many efforts have been made to improve its accuracy. However, less investment is conducted in aligning translations with human preferences, such as translation tones or styles. In this paper, a new method is proposed to effectively generate large-scale multilingual parallel corpora with specific translation preferences using Large Language Models (LLMs). Meanwhile, an automatic pipeline is designed to distill human preferences into smaller Machine Translation (MT) models for efficiently and economically supporting large-scale calls in online services. Experiments indicate that the proposed method takes the lead in translation tasks with aligned human preferences by a large margin. Meanwhile, on popular public benchmarks like WMT and Flores, on which our models were not trained, the proposed method also shows a competitive performance compared to SOTA works.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 9 of its 13 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

9 of 13 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model23 Jan
  2. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation16 Jan
  3. 2023Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decoding14 Nov

Other references · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  3. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  4. 2020Beyond English-Centric Multilingual Machine Translationcited by 561
  5. 2024A Survey on Multilingual Large Language Models: Corpora, Alignment, and Biascited by 84
  6. 2022MAD for Robust Reinforcement Learning in Machine Translationcited by 7

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Similar, not cited

Close in wording, no citation either way

  1. 2024Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
  2. 2024Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
  3. 2024A Preference-driven Paradigm for Enhanced Translation with Large Language Models
  4. 2025CM-Align: Consistency-based Multilingual Alignment for Large Language Models
  5. 2025A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
  6. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
  7. 2024Towards a Unified View of Preference Learning for Large Language Models: A Survey
  8. 2024Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation