# MetricX-24: The Google Submission to the WMT 2024 Metrics Shared Task

arXiv: https://arxiv.org/abs/2410.03983 · HTML: https://mlsanity.com/paper/2410.03983/

## Key facts

- Cited by 93 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 34 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://mlsanity.com/paper/2310.10482/) (2023)
- [CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task](https://mlsanity.com/paper/2209.06243/) (2022)
- [Scaling Up Models and Data with textttt5x and textttseqio](https://mlsanity.com/paper/2203.17189/) (2022)
- [To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation](https://mlsanity.com/paper/2107.10821/) (2021)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://mlsanity.com/paper/2004.14564/) (2020)
- [The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation](https://mlsanity.com/paper/2308.07286/) (2023)
- [Learning Compact Metrics for MT](https://mlsanity.com/paper/2110.06341/) (2021)
- [Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration](https://mlsanity.com/paper/2305.14324/) (2023)
- [DEMETR: Diagnosing Evaluation Metrics for Translation](https://mlsanity.com/paper/2210.13746/) (2022)
- [The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics](https://mlsanity.com/paper/2310.19792/) (2023)
- [PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation](https://mlsanity.com/paper/2406.18528/) (2024)
- [Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task](https://mlsanity.com/paper/2210.09683/) (2022)

## Built on by (most cited first, 30 of 93)

- [Tower+: Bridging Generality and Translation Specialization in Multilingual LLMs](https://mlsanity.com/paper/2506.17080/) (2025)
- [Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency](https://mlsanity.com/paper/2502.04964/) (2025)
- [How to Select Datapoints for Efficient Human Evaluation of NLG Models?](https://mlsanity.com/paper/2501.18251/) (2025)
- [BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation](https://mlsanity.com/paper/2502.04314/) (2025)
- [SEA-HELM: Southeast Asian Holistic Evaluation of Language Models](https://mlsanity.com/paper/2502.14301/) (2025)
- [Zero-shot Benchmarking: A Framework for Flexible and Scalable Automatic Evaluation of Language Models](https://mlsanity.com/paper/2504.01001/) (2025)
- [Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress](https://mlsanity.com/paper/2506.19571/) (2025)
- [MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task](https://mlsanity.com/paper/2510.24707/) (2025)
- [AskQE: Question Answering as Automatic Evaluation for Machine Translation](https://mlsanity.com/paper/2504.11582/) (2025)
- [Semantic Voting: A Self-Evaluation-Free Approach for Efficient LLM Self-Improvement on Unverifiable Open-ended Tasks](https://mlsanity.com/paper/2509.23067/) (2025)
- [Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs](https://mlsanity.com/paper/2512.16378/) (2025)
- [TranslateGemma Technical Report](https://mlsanity.com/paper/2601.09012/) (2026)
- [Extending Automatic Machine Translation Evaluation to Book-Length Documents](https://mlsanity.com/paper/2509.17249/) (2025)
- [Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation](https://mlsanity.com/paper/2601.07338/) (2026)
- [Translation as a Scalable Proxy for Multilingual Evaluation](https://mlsanity.com/paper/2601.11778/) (2026)
- [Batayan: A Filipino NLP benchmark for evaluating Large Language Models](https://mlsanity.com/paper/2502.14911/) (2025)
- [UNCERTAINTY-LINE: Length-Invariant Estimation of Uncertainty for Large Language Models](https://mlsanity.com/paper/2505.19060/) (2025)
- [TACTIC: Translation Agents with Cognitive-Theoretic Interactive Collaboration](https://mlsanity.com/paper/2506.08403/) (2025)
- [Translate Smart, not Hard: Cascaded Translation Systems with Quality-Aware Deferral](https://mlsanity.com/paper/2502.12701/) (2025)
- [You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation](https://mlsanity.com/paper/2503.24013/) (2025)
- [KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025](https://mlsanity.com/paper/2505.13036/) (2025)
- [Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation](https://mlsanity.com/paper/2509.20287/) (2025)
- [Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation](https://mlsanity.com/paper/2603.13045/) (2026)
- [Domain-Specific Translation with Open-Source Large Language Models: Resource-Oriented Analysis](https://mlsanity.com/paper/2412.05862/) (2024)
- [Theoretical Guarantees for Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2502.12685/) (2025)
- [TAT-R1: Terminology-Aware Translation with Reinforcement Learning and Word Alignment](https://mlsanity.com/paper/2505.21172/) (2025)
- [How to Evaluate Speech Translation with Source-Aware Neural MT Metrics](https://mlsanity.com/paper/2511.03295/) (2025)
- [DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains](https://mlsanity.com/paper/2511.10984/) (2025)
- [From Jack of All Trades to Master of One: Specializing LLM-based Autoraters to a Test Set](https://mlsanity.com/paper/2411.15387/) (2024)
- [Early-Exit and Instant Confidence Translation Quality Estimation](https://mlsanity.com/paper/2502.14429/) (2025)

## Abstract (arXiv)

In this paper, we present the MetricX-24 submissions to the WMT24 Metrics Shared Task and provide details on the improvements we made over the previous version of MetricX. Our primary submission is a hybrid reference-based/-free metric, which can score a translation irrespective of whether it is given the source segment, the reference, or both. The metric is trained on previous WMT data in a two-stage fashion, first on the DA ratings only, then on a mixture of MQM and DA ratings. The training set in both stages is augmented with synthetic examples that we created to make the metric more robust to several common failure modes, such as fluent but unrelated translation, or undertranslation. We demonstrate the benefits of the individual modifications via an ablation study, and show a significant performance increase over MetricX-23 on the WMT23 MQM ratings, as well as our new synthetic challenge set.