Summary of Ranking Entities Along Conceptual Space Dimensions with Llms: An Analysis Of Fine-tuning Strategies, by Nitesh Kumar et al.
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
by Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conceptual spaces represent entities based on their primitive semantic features, which are valuable but challenging to learn, particularly when modeling perceptual and subjective features. Recent research has explored distilling conceptual spaces from Large Language Models (LLMs) using zero-shot strategies, but these approaches have limitations. Our study focuses on ranking entities according to a given conceptual space dimension, which is essential for understanding the relationships between entities. However, we cannot fine-tune LLMs directly due to the scarcity of ground truth rankings for conceptual space dimensions. To address this issue, we utilize more readily available features as training data and investigate whether the resulting models can generalize to perceptual and subjective features. Our findings indicate that while there is some transferability, having at least some perceptual and subjective features in the training data is crucial for achieving optimal results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conceptual spaces help us understand things by looking at their basic building blocks. But it’s hard to learn these spaces, especially when we’re trying to figure out how people perceive or feel about something. Some researchers have been experimenting with using big language models to help with this task. The problem is that we can’t just directly train the model on this task because we don’t have enough examples to compare our answers to. So, we used easier-to-get data and asked if the model could still learn to rank things based on how they fit into a particular concept. Surprisingly, it worked! But only when we gave the model some hints about what people might think or feel. |
Keywords
* Artificial intelligence * Transferability * Zero shot
