# X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale

arXiv: https://arxiv.org/abs/2410.03115 · HTML: https://mlsanity.com/paper/2410.03115/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 71 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 30 of 54)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024)
- [A General Theoretical Paradigm to Understand Learning from Human Preferences](https://mlsanity.com/paper/2310.12036/) (2023)
- [ORPO: Monolithic Preference Optimization without Reference Model](https://mlsanity.com/paper/2403.07691/) (2024)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation](https://mlsanity.com/paper/2401.08417/) (2024)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [Massively Multilingual Neural Machine Translation](https://mlsanity.com/paper/1903.00089/) (2019)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)

## Built on by (most cited first, 30 of 36)

- [MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning](https://mlsanity.com/paper/2504.10160/) (2025)
- [Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs](https://mlsanity.com/paper/2506.17080/) (2025)
- [Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study](https://mlsanity.com/paper/2502.02481/) (2025)
- [R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning](https://mlsanity.com/paper/2502.19735/) (2025)
- [Cross-lingual Transfer of Reward Models in Multilingual Alignment](https://mlsanity.com/paper/2410.18027/) (2024)
- [Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training](https://mlsanity.com/paper/2504.01801/) (2025)
- [Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation](https://mlsanity.com/paper/2503.04554/) (2025)
- [See the Text: From Tokenization to Visual Reading](https://mlsanity.com/paper/2510.18840/) (2025)
- [Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models](https://mlsanity.com/paper/2506.13044/) (2025)
- [MT3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2505.19714/) (2025)
- [TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration](https://mlsanity.com/paper/2506.08403/) (2025)
- [Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data](https://mlsanity.com/paper/2504.14669/) (2025)
- [KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025](https://mlsanity.com/paper/2505.13036/) (2025)
- [M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation](https://mlsanity.com/paper/2510.13434/) (2025)
- [NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs](https://mlsanity.com/paper/2511.07003/) (2025)
- [Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models](https://mlsanity.com/paper/2602.11961/) (2026)
- [Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation](https://mlsanity.com/paper/2603.13045/) (2026)
- [Exploring the Translation Mechanism of Large Language Models](https://mlsanity.com/paper/2502.11806/) (2025)
- [CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences](https://mlsanity.com/paper/2511.07691/) (2025)
- [NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning](https://mlsanity.com/paper/2601.03790/) (2026)
- [Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model](https://mlsanity.com/paper/2505.17894/) (2025)
- [Who Watches the Watchmen? Humans Disagree With Translation Metrics on Unseen Domains](https://mlsanity.com/paper/2604.17393/) (2026)
- [TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation](https://mlsanity.com/paper/2508.08680/) (2025)
- [TowerVision: Understanding and Improving Multilinguality in Vision-Language Models](https://mlsanity.com/paper/2510.21849/) (2025)
- [Policy-based Sentence Simplification: Replacing Parallel Corpora with LLM-as-a-Judge](https://mlsanity.com/paper/2512.06228/) (2025)
- [CLewR: Curriculum Learning with Restarts for Machine Translation Preference Learning](https://mlsanity.com/paper/2601.05858/) (2026)
- [From Utterance to Vividity: Training Expressive Subtitle Translation LLM via Adaptive Local Preference Optimization](https://mlsanity.com/paper/2602.01068/) (2026)
- [LLM-Powered Automatic Translation and Urgency in Crisis Scenarios](https://mlsanity.com/paper/2602.13452/) (2026)
- [BabelSteering: Multilingual Safety Alignment via English Steering Vectors](https://mlsanity.com/paper/2608.16577/) (2026)
- [STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation](https://mlsanity.com/paper/2608.27161/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have achieved remarkable success across various NLP tasks with a focus on English due to English-centric pre-training and limited multilingual data. In this work, we focus on the problem of translation, and while some multilingual LLMs claim to support for hundreds of languages, models often fail to provide high-quality responses for mid- and low-resource languages, leading to imbalanced performance heavily skewed in favor of high-resource languages. We introduce **X-ALMA**, a model designed to ensure top-tier performance across 50 diverse languages, regardless of their resource levels. X-ALMA surpasses state-of-the-art open-source multilingual LLMs, such as Aya-101 and Aya-23, in every single translation direction on the FLORES-200 and WMT'23 test datasets according to COMET-22. This is achieved by plug-and-play language-specific module architecture to prevent language conflicts during training and a carefully designed training regimen with novel optimization methods to maximize the translation performance. After the final stage of training regimen, our proposed **A**daptive **R**ejection **P**reference **O**ptimization (**ARPO**) surpasses existing preference optimization methods in translation tasks.