mlsanityData to 14 Oct 2026

Remedy: Learning Machine Translation Evaluation from Human Preferences with Reward Modeling

arXiv 2504.13630 ↗First posted 18 Apr 2025cs.CLcited by 9

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

Abstract (arXiv)

A key challenge in MT evaluation is the inherent noise and inconsistency of human ratings. Regression-based neural metrics struggle with this noise, while prompting LLMs shows promise at system-level evaluation but performs poorly at segment level. In this work, we propose ReMedy, a novel MT metric framework that reformulates translation evaluation as a reward modeling task. Instead of regressing on imperfect human ratings directly, ReMedy learns relative translation quality using pairwise preference data, resulting in a more reliable evaluation. In extensive experiments across WMT22-24 shared tasks (39 language pairs, 111 MT systems), ReMedy achieves state-of-the-art performance at both segment- and system-level evaluation. Specifically, ReMedy-9B surpasses larger WMT winners and massive closed LLMs such as MetricX-13B, XCOMET-Ensemble, GEMBA-GPT-4, PaLM-540B, and finetuned PaLM2. Further analyses demonstrate that ReMedy delivers superior capability in detecting translation errors and evaluating low-quality translations.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 31 of its 56 references that are in the map. First posted on arXiv 18 Apr 2025.

Builds on

31 of 56 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation16 Jan
  2. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 29

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  6. 2017Deep reinforcement learning from human preferencescited by 3,912
  7. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  8. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  9. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  10. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  11. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,301
  12. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
17 more
  1. 2020COMET: A Neural Framework for MT Evaluationcited by 1,033
  2. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
  3. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 420
  4. 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 338
  5. 2024Skywork-Reward: Bag of Tricks for Reward Modeling in LLMscited by 327
  6. 2023xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detectioncited by 265
  7. 2021To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translationcited by 163
  8. 2023A Paradigm Shift in Machine Translation: Boosting Translation Performance of Large Language Modelscited by 89
  9. 2023Scaling up COMETKIWI: Unbabel-IST 2023 Submission for the Quality Estimation Shared Taskcited by 88
  10. 2023Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibrationcited by 74
  11. 2021A Statistical Analysis of Summarization Evaluation Metrics using Resampling Methodscited by 72
  12. 2022ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metricscited by 25
  13. 2024Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracycited by 25
  14. 2024Machine Translation Meta Evaluation through Translation Accuracy Challenge Setscited by 18
  15. 2024MT-Ranker: Reference-free machine translation evaluation by inter-system rankingcited by 14
  16. 2023Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performancecited by 10
  17. 2025Enhancing Human Evaluation in Machine Translation with Comparative Judgmentcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translationcited by 4
  2. 2025Investigating Test-Time Scaling with Reranking for Machine Translationcited by 3
  3. 2026What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translationcited by 2
  4. 2025Remedy-R: Generative Reasoning for Machine Translation Evaluation without Error Annotationscited by 1
  5. 2026PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translationcited by 1
  6. 2026STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translationcited by 1
  7. 2026Beyond Scalar Scores: Reinforcement Learning for Error-Aware Quality Estimation of Machine Translationcited by 0
  8. 2026Model-Based Quality Assessment for Massively Multilingual Parallel Datacited by 0
  9. 2026LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Editscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model
  2. 2024Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution
  3. 2024Is Preference Alignment Always the Best Option to Enhance LLM-Based Translation? An Empirical Analysis
  4. 2024Modeling User Preferences with Automatic Metrics: Creating a High-Quality Preference Dataset for Machine Translation
  5. 2023Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
  6. 2025TransEvalnia: Reasoning-based Evaluation and Ranking of Translations
  7. 2024Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings
  8. 2025MT-RewardTree: A Comprehensive Framework for Advancing LLM-Based Machine Translation via Reward Modeling