Developmentally Plausible Multimodal Language Models Are Highly Modular (2024)

Abstract: Large language models demonstrate emergent modularity, where functionally specialized components and circuits arise to handle specific tasks or task formats. If similar modules arise in models trained on more cognitively plausible datasets, it could inform debates surrounding what kinds of would be learnable given more human-like language learning signals. In this paper, we describe a multimodal vision-language model submitted to the BabyLM Challenge. Our model achieves similar performance to the best-performing architectures from last year, though visual information does not improve performance on text-only tasks over text-only models (in accordance with prior findings). To better understand how the model processes the evaluation tasks of the BabyLM Challenge, we leverage causal interpretability methods to locate the neurons that contribute to the model’s final decisions. We find that the models we train are highly modular: distinct components arise to process related tasks. Furthermore, on text-and-image tasks, adding or removing visual inputs causes the model to use distinct components to process the same textual inputs. This suggests that modal and task-specific specialization is efficiently learned, and that a high degree of functional specialization arises in even small-scale language models.

## introduction despite impressive capabilities across a wide range of tasks, language models (lms) remain highly data-inefficient: lms typically require orders of magnitude more data during pretraining than humans encounter over their entire lifetime @xcite . this inefficiency has driven interest in alternative approaches to language learning that leverage more human-like language learning scenarios. one such effort is the babylm challenge @xcite , which promotes the development of language models trained on the quantity of linguistic input that children receive when learning language. to create a more developmentally plausible training setup, the 2024 iteration of the challenge @xcite provides aligned image and text data. evaluating these more cognitively plausible models requires a focused analysis not only of how models behave, but also of the mechanisms foot_0 underlying their behaviors. conventional benchmarks are finite and often deploy identically distributed train/test splits, causing us to overlook key aspects of how models generalize. to address this, mechanistic interpretability has emerged as a framework for obtaining a more algorithmic understanding of how neural networks perform particular behaviors. this typically entails causally attributing model behavior to specific components, or causal graphs composed thereof. we conduct a study around one of the baseline architectures from the babylm challenge that incorporates both language and vision: the generative image transformer (git; @xcite . we train and evaluate a suite of language-only and multimodal models with this architecture to investigate the role of visual inputs in language learning. specifically, we first examine how different weighting schemes for text and image-text loss signals affect model performance and assess whether visual input offers any benefit for language learning. as expected, visual data leads to enhanced performance on multimodal benchmarks compared to text-only models. however, we find no significant benefit of visual data for performance on text-only benchmarks. this supports prior findings of a multimodal submission from last year's babylm challenge @xcite , as well as findings of @xcite . then, using attribution patching @xcite , we identify the most causally important neu-rons in git's text decoder across tasks. this analysis reveals a high level of modularity, foot_1 with separate internal mechanisms being deployed even for slightly different subtasks of the same task. most surprisingly, the same textual input is processed differently in the text decoder depending on whether visual inputs are present. this suggests that visual inputs do not merely add to textual information, but rather activate distinct mechanisms in the model's language processing components. these findings suggest that modal and task-specific specialization is efficiently learnable in human-like learning scenarios, even in the absence of human-like learning biases. 3 these findings extend prior work on emergent modularity in pre-trained language models (e.g., @xcite @xcite to a more cognitively plausible training scenario, thus allowing us to make more convincing claims as to what kinds of linguistic functional specializations can arise from humanlike language learning signals. our main contributions are as follows: recent related work has investigated whether visual inputs can aid in word learning, finding largely negative results-but crucially, visual inputs are helpful in the kinds of low-resource scenarios we investigate @xcite . the 2023 babylm challenge received many multimodal submissions; most relevant to ours is the text-and-vision submission of @xcite . mechanistic interpretability mechanistic interpretability methods allow us to more deeply understand where and how particular tasks are accomplished in a neural network. this paper focuses more on localizing than qualitatively explaining model behavior-but localization can itself reveal whether certain behaviors are performed using the same underlying mechanisms. for example, one line of work aims to causally quantify whether the most important neurons for a particular task overlap with those from highly related tasks in language models (e.g., @xcite . there also exist investigations of the mechanisms underlying how visionlanguage models accomplish particular tasks (e.g., @xcite . past work has used other (not always causal) methods to discover that language models are highly modular; this includes work with small-scale cnn and lstmbased models @xcite , as well as large transformer-based models @xcite . our work extends this literature through analyses of developmentally plausible multimodal language models. we investigate whether these models use similar mechanisms to perform diverse natural language processing (nlp) tasks, and whether they use the same mechanisms to perform the same tasks with and without image data. while our models are not directly comparable to human learners due to differing inductive biases and a relatively small quantity of visual inputs, they nonetheless provide evidence as to the kinds of mechanisms that are learnable from a realistic language learning dataset. ## results we train and evaluate four weighting configurations for the multimodal model and two for the textonly model; for each configuration, we average across three random seeds. detailed information on the learning progress of each model is provided in app. b. ## discussion we find little neuron overlap between vision-andtext and text-only variants of the same tasks. this suggests a significant degree of modularity in smallscale multimodal language models. 13 this raises important questions: is component sharing between unimodal and multimodal processing mechanisms of the same task desirable? can it serve as a signal of effective merging of information across modalities? to investigate, future research could explore the relationship between neuron overlap and task performance, ideally across diverse architectures. many causes could explain the minimal overlap across similar benchmarks. first, the conceptual space in the model representations could be such that there are few features or skills in common across tasks; thus, to the model, these tasks have little in common. investigating this possibility would involve a more thorough qualitative analysis of the features implicated in performing each task. second, different tasks may share features, but the model might learn domain-specific versions of qualitatively identical features. follow-up research could vary task formats-for example, by paraphrasing all examples-and analyze whether this changes the top neurons. that said, there is some correlation between task similarity and component overlap within a benchmark. this serves as a sanity check, and also indicates that even small models tend to share processing mechanisms across closely related tasks with similar formats. this is a more parameter-efficient strategy compared to representing similar tasks in a fully modular fashion. is a high degree of task modularity desirable? some argue that emergent modularity can be harnessed for better generalization in language models (e.g., @xcite ; it could also enable more fine-grained mechanistic understanding and con-trol. however, modularity will generally result in reduced parameter-efficiency. it could also be a signal that a model is not efficiently compressing information in a generalizable way, such that it must relearn similar phenomena for distinct task settings. we speculate that there exist more or less desirable types and extents of modularity in neural language models, and that classifying these types of modularity could be especially helpful in assessing parameter-(in)efficiency. relatedly, when speaking of modularity, it is essential to distinguish between two types of neural modules: (i) skill-related neural groups that share general abilities independent of specific tasks, and (ii) task-related neural groups that are specialized for particular task formats. in our experiments, we predominantly observe the latter. from an engineering perspective, there is no clear indication whether this would enhance performance or efficiency. however, if one's goal is to model human language processing, perhaps modularity could be a useful signal. certain regions of the brain specialize toward particular tasks, even in the presence of similar visual stimuli across tasks @xcite ; different specialized regions for the same task can also arise given sufficiently distinct stimuli @xcite . our findings agree with both. whether emergent task modules in developmentally plausible language models correspond to comparable regions in the human brain remains an interesting open question. ## conclusion developmentally plausible multimodal language models exhibit a high degree of modularity. furthermore, adding visual inputs changes how the text decoder processes a task, and increases the amount of shared components between tasks. our findings highlight the types of functional specialization that can arise in language models trained on developmentally plausible data, and raise questions about trade-offs between sample-efficiency, parameter-efficiency, and cognitive plausibility.