@inproceedings{klerings-etal-2024-developmentally,
title = "Developmentally Plausible Multimodal Language Models Are Highly Modular",
author = "Klerings, Alina and
Bartelt, Christian and
Mueller, Aaron",
editor = "Hu, Michael Y. and
Mueller, Aaron and
Ross, Candace and
Williams, Adina and
Linzen, Tal and
Zhuang, Chengxu and
Choshen, Leshem and
Cotterell, Ryan and
Warstadt, Alex and
Wilcox, Ethan Gotlieb",
booktitle = "The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.conll-babylm.10/",
pages = "118--139",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Developmentally Plausible Multimodal Language Models Are Highly Modular
%A Klerings, Alina
%A Bartelt, Christian
%A Mueller, Aaron
%Y Hu, Michael Y.
%Y Mueller, Aaron
%Y Ross, Candace
%Y Williams, Adina
%Y Linzen, Tal
%Y Zhuang, Chengxu
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F klerings-etal-2024-developmentally
%X 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.
%U https://aclanthology.org/2024.conll-babylm.10/
%P 118-139
Markdown (Informal)
[Developmentally Plausible Multimodal Language Models Are Highly Modular](https://aclanthology.org/2024.conll-babylm.10/) (Klerings et al., CoNLL-BabyLM 2024)
ACL