Investigating Critical Period Effects in Language Acquisition through Neural Language Models (2407.19325v2)

Abstract: Humans appear to have a critical period (CP) for language acquisition: Second language (L2) acquisition becomes harder after early childhood, and ceasing exposure to a first language (L1) after this period (but not before) typically does not lead to substantial loss of L1 proficiency. It is unknown whether these CP effects result from innately determined brain maturation or as a stabilization of neural connections naturally induced by experience. In this study, we use LLMs (LMs) to test the extent to which these phenomena are peculiar to humans, or shared by a broader class of language learners. We vary the age of exposure by training LMs on language pairs in various experimental conditions, and find that LMs, which lack any direct analog to innate maturational stages, do not show CP effects when the age of exposure of L2 is delayed. Our results contradict the claim that CP effects are an inevitable result of statistical learning, and they are consistent with an innate mechanism for CP effects. We show that we can reverse-engineer the CP by introducing a regularizer partway through training to simulate a maturational decrease in plasticity. All in all, our results suggest that L1 learning on its own may not be enough to induce a CP, and additional engineering is necessary to make LLMs more cognitively plausible.

• The paper demonstrates that neural language models do not inherently exhibit human-like critical period effects, challenging experiential learning theories.
• The study employs models like GPT-2 and RoBERTa with sequential language exposure to analyze L1 attrition and L2 acquisition dynamics.
• The research shows that applying an Elastic Weight Consolidation regularizer artificially induces CP effects, suggesting a role for biological maturational constraints.

An In-depth Analysis of Critical Period Effects in Language Acquisition Using Neural LLMs

The paper "Investigating Critical Period Effects in Language Acquisition through Neural LLMs," authored by Constantinescu et al., explores the concept of critical period (CP) in language acquisition through the lens of current neural LLMs (LMs). This investigation aims to discern the underlying mechanisms of CP effects—whether they are innately predetermined or a natural byproduct of experiential learning.

Core Premise and Experimentation

The authors set out to test if phenomena associated with CPs in human language acquisition can be observed in LLMs, which lack biologically innate maturational stages. To do so, they designed rigorous experiments varying the age of exposure to second language (L2) in neural networks, analyzing how these models learn and potentially forget languages when exposed to them at different "ages" during training. The models used include autoregressive models like GPT-2 and masked LLMs such as RoBERTa.

Key Findings

1. Absence of Natural CP Effects in LMs: The paper finds that LMs do not inherently exhibit CP effects related to L2 acquisition. When trained sequentially on different languages, these models do not show the expected difficulty in learning a second language at "older" ages—contrary to human learners who show diminished performance in acquiring L2 post the typical critical period.
2. Catastrophic Forgetting Mirrors Lack of CP for L1 Attrition: In another set of experiments focusing on first language (L1) attrition, the models forget previously learned languages when exposed to a new one, indicating a lack of CP effects for L1 retention. This suggests that neural networks are characteristically prone to catastrophic forgetting, unlike humans who generally retain L1 proficiency despite reduced exposure.
3. Simulating CP through Regularization: Interestingly, the authors demonstrate that by introducing an Elastic Weight Consolidation (EWC) regularizer midway through training, mimicking a reduction in neural plasticity, they can artificially induce CP-like effects. This indicates that innate reductions in plasticity, which could be analogous to biological maturational constraints, might be necessary for CP phenomena.

Implications

The findings carry substantial implications for both theoretical understanding and practical advancements in AI and cognitive modeling:

• Refutation of the Experiential Hypothesis: The paper provides strong evidence against the hypothesis that CP effects are purely a consequence of general statistical learning. This challenges prior assertions made by connectionist models regarding entrenchment as an inevitable outcome of learning dynamics.
• Support for Biologically-Driven CP Mechanisms: While the results don't definitively prove the necessity of innate CP mechanisms, they are consistent with neurobiological theories suggesting that certain critical periods in human language development are biologically programmed.
• Improving Cognitive Plausibility in LMs: From an engineering perspective, inducing CP effects through methods like EWC could help make LMs more cognitively plausible. This can aid in creating models that simulate human-like learning trajectories, potentially enhancing their application in understanding human cognition.

Speculation on Future Developments in AI

The exploration into CP effects with neural LLMs opens several avenues for further research:

• Modularity in Multilingual Models: Incorporating bilingualism effects through potential architectural changes could represent a promising direction. This might involve modular designs which reflect the human brain's manner of managing multiple languages.
• Multimodal Training Regimens: Considering multimodal inputs during language acquisition can further bridge the gap between human and machine learning processes.

In summary, Constantinescu et al. offer a compelling paper into the intricacies of CPs in language acquisition, utilizing neural LLMs as a testbed. Their insights not only push the boundaries of cognitive modeling with LMs but also encourage a reevaluation of longstanding hypotheses in language acquisition theories. As research progresses, such findings could inform the design of more sophisticated AI systems that align closer with human-like learning paradigms.