Papers by Usashi Chatterjee
Extracting Conceptual Spaces from LLMs Using Prototype Embeddings (2025.findings-emnlp)
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| Challenge: | Conceptual spaces represent entities and concepts using cognitively meaningful dimensions . practical methods for extracting conceptual spaces are currently lacking . |
| Approach: | They propose a strategy in which features are encoded by embedding a description of a corresponding prototype. |
| Outcome: | The proposed approach is highly effective. |
Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces (2023.emnlp-main)
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| Challenge: | Conceptual spaces are constructed from a set of quality dimensions, which are usually learned from human judgements, which means that applications of conceptual spaces are limited to narrow domains. |
| Approach: | They propose to use Large Language Models to learn perceptually grounded representations by comparing them to larger models of the BERT family. |
| Outcome: | The proposed models outperform the largest model, despite being 2 to 3 orders of magnitude smaller. |
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies (2024.findings-acl)
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| Challenge: | Conceptual spaces represent entities in terms of their primitive semantic features. |
| Approach: | They argue that conceptual spaces should be used alongside knowledge graphs in many settings to model entities in terms of their primitive semantic features. |
| Outcome: | The proposed model can rank entities according to a given conceptual space dimension but ground truth rankings for conceptual space dimensions are rare. |
Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings (2024.findings-acl)
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| Challenge: | Concept embeddings are a useful and efficient mechanism for injecting commonsense knowledge into downstream tasks. |
| Approach: | They propose to model commonalities in concepts by capturing a more diverse range of commonsense properties. |
| Outcome: | The proposed model captures a more diverse range of commonsense properties and improves ontology completion and ultra-fine entity typing tasks. |
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies (2023.emnlp-main)
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| Challenge: | Existing work on decontextualised concept embeddings from language models has focused on capturing taxonomic structure in concepts. |
| Approach: | They propose a strategy for identifying what different concepts have in common with others and representing them in terms of their properties. |
| Outcome: | The proposed approach improves the performance of state-of-the-art models for a multi-label classification problem. |