What Artificial Neural Networks Can Tell Us About Human Language Acquisition (2208.07998v2)

Abstract: Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments and biases of current artificial learners and humans diverge in ways that weaken the impact of the evidence obtained from learning simulations. For example, today's most effective neural LLMs are trained on roughly one thousand times the amount of linguistic data available to a typical child. To increase the relevance of learnability results from computational models, we need to train model learners without significant advantages over humans. If an appropriate model successfully acquires some target linguistic knowledge, it can provide a proof of concept that the target is learnable in a hypothesized human learning scenario. Plausible model learners will enable us to carry out experimental manipulations to make causal inferences about variables in the learning environment, and to rigorously test poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in humans on the basis of speculations about learnability. Comparable experiments will never be possible with human subjects due to practical and ethical considerations, making model learners an indispensable resource. So far, attempts to deprive current models of unfair advantages obtain sub-human results for key grammatical behaviors such as acceptability judgments. But before we can justifiably conclude that language learning requires more prior domain-specific knowledge than current models possess, we must first explore non-linguistic inputs in the form of multimodal stimuli and multi-agent interaction as ways to make our learners more efficient at learning from limited linguistic input.

• The paper demonstrates that ablation studies in ANNs can isolate learnable linguistic features resembling human language acquisition.
• It employs unsupervised testing and the BLiMP benchmark to quantitatively assess models' syntactic and semantic capabilities.
• The findings suggest that incorporating multimodal data in learning environments may better align ANN biases with human learning conditions.

Insights into Human Language Acquisition Through Artificial Neural Networks

The paper "What Artificial Neural Networks Can Tell Us About Human Language Acquisition" by Alex Warstadt and Samuel R. Bowman explores the potential of using artificial neural networks (ANNs) to contribute to debates around human language acquisition. The authors argue that despite the current limitations and discrepancies between human learners and ANNs, the latter offer a scalable and ethically viable method to explore learnability questions that are difficult or impossible to address directly through human subjects.

Key Considerations

The authors focus on creating a learning paradigm using ANNs which mirrors human language learning to some degree, despite current large-scale LLMs being trained on datasets vastly larger than those available to a typical child. The work lays out key methodologies for leveraging ANNs to derive insights into human learning, emphasizing the importance of modeling these networks to mimic human learning conditions more closely.

The central methodological suggestion of the paper is to employ ablation studies. These entail recreating learning conditions that lack certain hypothesized advantages to ascertain whether specific linguistic capabilities can still emerge. This approach underscores the potential of model learners to provide proofs of concept for what is inherently learnable and to refute the necessity of certain environmental or innate advantages traditionally attributed to human learners.

Linguistic Evaluation of Model Learners

The authors outline several methods to assess model learners, focusing on their linguistic capabilities. Unsupervised testing is highlighted as pivotal, leveraging probabilistic LLMs (LMs) to predict the likelihood of word sequences and simulate acceptability judgments that align or differ from human intuition.

Emphasis is placed on the BLiMP benchmark, which evaluates LLM scoring across a variety of complex linguistic phenomena, ranging from subject-verb agreement to syntactic and semantic rules that require intricate understanding. The performance of models in these tasks, especially when trained on human-scale datasets, provides substantive grounds for effective comparison with human linguistic abilities.

The Role of Learning Environment and Modality

The environment from which a model learns is a significant focus. Current models are mostly text-only and do not replicate the multimodal and interactive nature of human learning environments. Future research is encouraged to incorporate non-linguistic inputs, such as images and interaction with virtual agents, to foster a more comprehensive understanding and simulation of human language acquisition conditions.

Significant improvements in ANN architectures, such as recurrent networks and Transformers, have advanced models' capacity to simulate linguistic behavior. However, these improvements underscore an ongoing need for aligning the training conditions of models more closely with the expected human experience, thereby reducing reliance on vast amounts of text data.

Inductive Bias and Innate Advantages

A rigorous comparison of ANNs and humans necessitates understanding their respective inductive biases. While current neural models show considerable work remains to align their bias with human-like learning, it is inferred they neither possess explicit language-specific advantages nor showcase inherent compositional or hierarchical biases before extensive training. This positions them as robust platforms to explore the nature of these biases when introduced via simulated human-like environmental constraints.

Implications and Future Directions

The potential implications of this paper are wide-ranging, extending both the theoretical understanding of linguistic learning and the practical applications of artificial learners in fields such as natural language processing and cognitive modeling. Future research directions include further refining model architectures to unify sensory data and interactive learning objectives, as well as enhancing the alignment of ANNs' inductive biases with those hypothesized for humans.

This work advocates for a nuanced approach to employing ANNs in linguistic studies: while challenges persist, leveraging model learners offers novel insights into learnability and the specific conditions under which human-like language capabilities might emerge. Incremental enhancements in creating plausible learning environments can significantly bolster the relevance of computational models to longstanding debates in language acquisition.