How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (2023.acl-long)
Copied to clipboard
| 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. |
| Approach: | They train LSTMs and Transformers on text from the CHILDES corpus and evaluate what they learn about English yes/no questions. |
| Outcome: | The proposed models perform well at capturing the surface statistics of child-directed speech, but generalize more consistent with an incorrect linear rule than the correct hierarchical rule. |
Similar Papers
Semantic Training Signals Promote Hierarchical Syntactic Generalization in Transformers (2024.emnlp-main)
Copied to clipboard
| Challenge: | Neural networks without hierarchical biases struggle to learn linguistic rules that come naturally to humans . et al., 2018: Transformers trained on form and meaning favor hierarchically generalization more than those trained on forms alone. |
| Approach: | They examine whether neural networks without hierarchical biases can generalize more like humans . they find that Transformers trained on form and meaning favor hierarchic generalization . |
| Outcome: | The proposed neural networks perform better on syntactic evaluations when trained on form and meaning compared to those trained on forms alone. |
Learning Syntax Without Planting Trees: Understanding Hierarchical Generalization in Transformers (2025.tacl-1)
Copied to clipboard
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. |
| Approach: | They investigate sources of inductive bias in transformer models and their training that could cause such preference for hierarchical generalization. |
| Outcome: | The proposed model can generalize to novel syntactic forms without explicit bias . the proposed model is able to generalize on a dataset with a hierarchical grammar . |
Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks (2020.tacl-1)
Copied to clipboard
| Challenge: | Inductive biases can arise from any aspect of the model architecture, study finds . we investigate which architectural factors affect how models generalize . |
| Approach: | They investigate which architectural factors affect generalization behavior of neural network models . they use English question formation and English tense reinflection as test cases . |
| Outcome: | The findings suggest that human-like generalization requires architectural syntactic structure. |
How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (2023.acl-long)
Copied to clipboard
| 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 . |
| Approach: | They find that pre-training can teach language models to rely on hierarchical syntactic features . authors: this suggests that in cognitively plausible language acquisition settings, models may be more data-efficient . |
| Outcome: | a recent study shows that pre-training can teach language models to rely on hierarchical features . the findings suggest that in plausible language acquisition settings, language models may be more data-efficient than previously thought . |
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners? (2025.coling-main)
Copied to clipboard
| Challenge: | Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. |
| Approach: | They investigate the advantage of grounded language acquisition over visual input to improve syntactic generalization. |
| Outcome: | The proposed model is less efficient than humans in language acquisition . it shows that visual input helps syntactic generalization, but not vision . |
Revisiting the Hierarchical Multiscale LSTM (C18-1)
Copied to clipboard
| Challenge: | Hierarchical Multiscale LSTM model learns structure from character input . high complexity of architecture, training and implementations might hinder its applicability . |
| Approach: | They propose to reproduce and ablate hierarchical multiscale LSTM language model and show that simplifying certain aspects of the architecture can improve its performance. |
| Outcome: | The proposed model performs better when simplified and linguistic units are learned by different levels of the model. |
Grokking of Hierarchical Structure in Vanilla Transformers (2023.acl-short)
Copied to clipboard
| Challenge: | a recent study has shown that neural sequence models like transformers can generalize hierarchically when training for extended periods. |
| Approach: | They show that transformers can learn to generalize hierarchically after long training periods . they call this phenomenon structural grokking, which exhibits inverted U-shaped scaling in model depth . |
| Outcome: | The proposed model generalizes better than both very deep and very shallow models on multiple datasets. |
LSTMs Compose—and Learn—Bottom-Up (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work in NLP shows that LSTMs capture compositional structure in language data. |
| Approach: | They propose to measure the decompositional interdependence between word meanings in an LSTM based on their gate interactions. |
| Outcome: | The proposed model can model syntactic relationships rather than learning the longer-range relations independently. |
Data Drives Unstable Hierarchical Generalization in LMs (2025.emnlp-main)
Copied to clipboard
| 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 . |
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have not investigated the relationship between a token's frequency in the training corpus and syntactic properties models learn about it. |
| Approach: | They develop controlled experiments that probe models’ syntactic nominal number and verbal argument structure generalizations for tokens seen as few as two times during training. |
| Outcome: | The proposed models can make syntactic generalizations for tokens seen as few as two times during training and transfer them to transformed contexts. |