mlsanityData to 7 Oct 2026

Evaluating the Values of Sources in Transfer Learning

arXiv 2104.12567 ↗First posted 26 Apr 2021cs.CLcited by 11

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Abstract (arXiv)

Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP). However, when training a transfer model over multiple sources, not every source is equally useful for the target. To better transfer a model, it is essential to understand the values of the sources. In this paper, we develop SEAL-Shap, an efficient source valuation framework for quantifying the usefulness of the sources (e.g., domains/languages) in transfer learning based on the Shapley value method. Experiments and comprehensive analyses on both cross-domain and cross-lingual transfers demonstrate that our framework is not only effective in choosing useful transfer sources but also the source values match the intuitive source-target similarity.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 26 Apr 2021.

Builds on

26 of 54 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  3. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  4. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  5. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  6. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 913
  7. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  8. 2017Word Translation Without Parallel Datacited by 480
  9. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
  10. 2019Linguistic Knowledge and Transferability of Contextual Representationscited by 382
  11. 2019The many Shapley values for model explanationcited by 341
  12. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
14 more
  1. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 279
  2. 2016Understanding Neural Networks through Representation Erasurecited by 223
  3. 2020Problems with Shapley-value-based explanations as feature importance measurescited by 198
  4. 2019Choosing Transfer Languages for Cross-Lingual Learningcited by 143
  5. 2018Pathologies of Neural Models Make Interpretations Difficultcited by 140
  6. 2020Exploring and Predicting Transferability across NLP Taskscited by 126
  7. 2020Neuron Shapley: Discovering the Responsible Neuronscited by 98
  8. 2020On the Tractability of SHAP Explanationscited by 92
  9. 2017Adversarial Multi-task Learning for Text Classificationcited by 91
  10. 2017Learning to select data for transfer learning with Bayesian Optimizationcited by 64
  11. 2020Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Datasetcited by 34
  12. 2019Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsingcited by 11
  13. 2019Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?cited by 9
  14. 2020Interpretable feature subset selection: A Shapley value based approachcited by 3

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Cited by year

Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 2.

11 papers.

  1. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 84
  2. 2023Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Taskscited by 53
  3. 2022CS-Shapley: Class-wise Shapley Values for Data Valuation in Classificationcited by 32
  4. 2025CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and Debuggingcited by 31
  5. 2023Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solutioncited by 22
  6. 2023Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Valuescited by 21
  7. 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
  8. 2022Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policiescited by 10
  9. 2023Efficient Shapley Values Estimation by Amortization for Text Classificationcited by 4
  10. 2025Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuningcited by 2
  11. 2024Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptationcited by 0