Cross-Modal Taxonomic Generalization in (Vision-) Language Models
arXiv:2603.07474v1 Announce Type: new Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input comes from a different modality -- in our case, in a vision-language model (VLM), where a pretrained LM is aligned with a pretrained image encoder. As a case study, we focus on the task of predicting hypernyms of objects represented in images. We do so in a VLM setup where the image encoder and LM are kept frozen, and only the intermediate mappings are learned. We progressively deprive the VLM of explicit evidence for hypernyms, and test whether knowledge of hypernyms is recoverable from the LM. We find that the LMs we study can recover this knowledge and generalize even in the most extreme version of this experiment (when the model receives no evidence of a hypernym during training). Additional experiments suggest that th
arXiv:2603.07474v1 Announce Type: new Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grounded evidence? We study this question for a scenario where part of the input comes from a different modality -- in our case, in a vision-language model (VLM), where a pretrained LM is aligned with a pretrained image encoder. As a case study, we focus on the task of predicting hypernyms of objects represented in images. We do so in a VLM setup where the image encoder and LM are kept frozen, and only the intermediate mappings are learned. We progressively deprive the VLM of explicit evidence for hypernyms, and test whether knowledge of hypernyms is recoverable from the LM. We find that the LMs we study can recover this knowledge and generalize even in the most extreme version of this experiment (when the model receives no evidence of a hypernym during training). Additional experiments suggest that this cross-modal taxonomic generalization persists under counterfactual image-label mappings only when the counterfactual data have high visual similarity within each category. Taken together, these findings suggest that cross-modal generalization in LMs arises as a result of both coherence in the extralinguistic input and knowledge derived from language cues.
Executive Summary
This article explores the interplay between semantic representations learned by language models from surface form alone and those learned from more grounded evidence, such as images. The study focuses on a vision-language model setup, where a pretrained language model is aligned with a pretrained image encoder. The results show that language models can recover knowledge of hypernyms and generalize even in the absence of explicit evidence, suggesting that cross-modal generalization arises from both coherence in extralinguistic input and knowledge derived from language cues.
Key Points
- ▸ Language models can recover knowledge of hypernyms without explicit evidence
- ▸ Cross-modal taxonomic generalization persists under counterfactual image-label mappings with high visual similarity
- ▸ Knowledge derived from language cues contributes to cross-modal generalization
Merits
Novel Experimental Design
The study's design allows for a nuanced understanding of the interplay between language and vision in cross-modal generalization
Insights into Language Model Capabilities
The findings provide new insights into the capabilities of language models to learn and generalize from multimodal data
Demerits
Limited Generalizability
The study's focus on a specific task and dataset may limit the generalizability of the findings to other domains and tasks
Dependence on Pretrained Models
The study's reliance on pretrained language and image models may introduce biases and limitations that are not fully explored
Expert Commentary
The study's findings have significant implications for our understanding of the interplay between language and vision in cross-modal generalization. The results suggest that language models are capable of learning and generalizing from multimodal data, even in the absence of explicit evidence. However, the study's limitations, such as its reliance on pretrained models and limited generalizability, highlight the need for further research in this area. As the development of multimodal learning models continues to advance, it is essential to consider the implications of these findings for real-world applications and policy frameworks.
Recommendations
- ✓ Further research on the development of more explainable and transparent multimodal learning models
- ✓ Investigation into the applicability of the study's findings to other domains and tasks, such as multimodal sentiment analysis and visual dialogue systems