Papers by Christopher J. MacLellan
Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision–Language Models (2026.findings-acl)
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| Challenge: | a long tradition in cognitive science treats concreteness as a graded dimension of conceptual representation . concrete words benefit from richer sensory codes and exhibit robust behavioral advantages over abstract words . |
| Approach: | They compare vision-language models with text-only large language models to test their concreteness . they find that VLMs show more human-like sensitivity to concreteness than LLMs . |
| Outcome: | The proposed model-based training improves on the Llama text backbones and Llma Vision counterparts. |
CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling (2026.findings-acl)
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| Challenge: | Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. |
| Approach: | They propose a lifelong hierarchical topic model based on incremental probabilistic concept formation that constructs semantic hierarchies online without predefining the number of topics. |
| Outcome: | The proposed model achieves strong topic coherence, stable topics over time, and high-quality hierarchies without predefining the number of topics. |