mlsanityData to 7 Oct 2026

ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models

arXiv 2406.09334 ↗First posted 13 Jun 2024cs.CLcited by 20

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Abstract (arXiv)

Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks, mitigating computational costs associated with model capacity and data for fine-tuning. Our paper presents ProxyLM, a scalable task- and language-agnostic framework designed to predict the performance of LMs using proxy models. These proxy models act as surrogates, approximating the performance of the LM of interest. By leveraging these proxy models, ProxyLM significantly reduces computational overhead in task evaluations, achieving up to a 37.08x speedup over traditional methods, even with our smallest proxy models. Our results across multiple multilingual NLP tasks and various robustness tests demonstrate that ProxyLM not only adapts well to previously unseen languages in pre-trained LMs, but also generalizes effectively across different datasets, outperforming the state-of-the-art by at least 1.78x in terms of root-mean-square error (RMSE).

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 31 of its 60 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

31 of 60 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
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  9. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  10. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
  11. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  12. 2020The State and Fate of Linguistic Diversity and Inclusion in the NLP Worldcited by 789
19 more
  1. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  2. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  3. 2020Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translationcited by 277
  4. 2019WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipediacited by 221
  5. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  6. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 142
  7. 2018When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?cited by 125
  8. 2018Universal Neural Machine Translation for Extremely Low Resource Languagescited by 66
  9. 2020Predicting Performance for Natural Language Processing Taskscited by 49
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  11. 2023Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networkscited by 37
  12. 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
  13. 2021Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translationcited by 29
  14. 2021Towards More Fine-grained and Reliable NLP Performance Predictioncited by 26
  15. 2021Predicting the Performance of Multilingual NLP Modelscited by 24
  16. 2020Evaluating Gender Bias in Speech Translationcited by 18
  17. 2024LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexiconscited by 11
  18. 2024MINERS: Multilingual Language Models as Semantic Retrieverscited by 11
  19. 2024LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalizationcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 8.

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  1. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  2. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  3. 2024URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Basecited by 21
  4. 2024MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferencescited by 15
  5. 2024MetaMetrics-MT: Tuning Meta-Metrics for Machine Translation via Human Preference Calibrationcited by 6
  6. 2026Learning to Ideate for Machine Learning Engineering Agentscited by 6
  7. 2025Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountabilitycited by 5
  8. 2025Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+cited by 3
  9. 2025Can Small Training Runs Reliably Guide Data Curation? Rethinking Proxy-Model Practicecited by 3
  10. 2025Simple Additions, Substantial Gains: Expanding Scripts, Languages, and Lineage Coverage in URIEL+cited by 2
  11. 2025IndoPref: A Multi-Domain Pairwise Preference Dataset for Indonesiancited by 1
  12. 2025Anticipatory Evaluation of Language Modelscited by 1
8 more
  1. 2026SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scalingcited by 1
  2. 2026TuneAhead: Predicting Fine-tuning Performance Before Full Training Beginscited by 1
  3. 2025Rethinking what Matters: Effective and Robust Multilingual Realignment for Low-Resource Languagescited by 0
  4. 2026DEPART: DEcomposing PARiTy across Multilingual LLMscited by 0
  5. 2026A Risk Decomposition Framework for Pre-Hoc Fine-Tuning Predictioncited by 0
  6. 2026Typological Feature Prediction with Large Language Models: An In-Context Learning Approachcited by 0
  7. 2026Online Language Adaptive Sampling for Better Distributed Cross-lingual Gainscited by 0
  8. 2026Language Distances are Practical for Equitable Cross-Lingual Transfercited by 0