| Challenge: | Early in training, LMs can behave like n-gram models but eventually learn tree-based syntactic rules and generalize out of distribution (OOD). |
| Approach: | They study how complex data drives hierarchical rules, while less complex encourages shortcut learning . they find a model uses rules to generalize if its training data is *diverse* . |
| Outcome: | The proposed model learns to generalize hierarchically if its training data is complex . a model learn if it includes center-embedded clauses, a special syntactic structure . |
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| Challenge: | Inductive biases can arise from any aspect of the model architecture, study finds . we investigate which architectural factors affect how models generalize . |
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Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models (2022.findings-acl)
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| Challenge: | Sequence-to-sequence models fail to generalize in hierarchy-sensitive manner when performing syntactic transformations. |
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Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve? (2024.emnlp-main)
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| Challenge: | In the last decade, the generalization and adaptation abilities of deep learning models were evaluated on fixed training and test distributions. |
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How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (2023.acl-long)
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| Challenge: | LSTMs and Transformers perform well at capturing the surface statistics of child-directed speech, but both model types generalize in a way consistent with an incorrect linear rule than the correct hierarchical rule. |
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Does Vision Accelerate Hierarchical Generalization in Neural Language Learners? (2025.coling-main)
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| Challenge: | Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. |
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Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers (2025.tacl-1)
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Kabir Ahuja, Vidhisha Balachandran, Madhur Panwar, Tianxing He, Noah A. Smith, Navin Goyal, Yulia Tsvetkov
| Challenge: | Inductive biases in transformers can cause hierarchical generalization without explicitly encoding structural bias. |
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How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (2023.acl-long)
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| Challenge: | a recent study found that pre-training can teach language models to rely on hierarchical syntactic features . aaron ramirez: we find that pretraining on simpler language induces a hierarchic bias . |
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Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
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Data Factors for Better Compositional Generalization (2023.emnlp-main)
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| Challenge: | Recent diagnostic datasets on compositional generalization expose severe problems . state-of-the-art models trained on larger and more general datasets show better generalization ability . |
| Approach: | They conduct an empirical analysis by training Transformer models on a variety of training sets with different data factors including dataset scale, pattern complexity, example difficulty, etc. |
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Revisiting Generalization Across Difficulty Levels: It’s Not So Easy (2026.eacl-long)
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| Challenge: | Existing research is mixed regarding whether training on easier or harder data leads to better results. |
| Approach: | They examine how well large language models generalize across different task difficulties by using a large dataset and a well-established difficulty metric. |
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