# Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies

arXiv: https://arxiv.org/abs/2402.15337 · HTML: https://mlsanity.com/paper/2402.15337/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [How Much Knowledge Can You Pack Into the Parameters of a Language Model?](https://mlsanity.com/paper/2002.08910/) (2020)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [KG-BERT: BERT for Knowledge Graph Completion](https://mlsanity.com/paper/1909.03193/) (2019)
- [Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://mlsanity.com/paper/2306.17563/) (2023)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [Linearly Mapping from Image to Text Space](https://mlsanity.com/paper/2209.15162/) (2022)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [Crawling the Internal Knowledge-Base of Language Models](https://mlsanity.com/paper/2301.12810/) (2023)
- [BERTMap: A BERT-based Ontology Alignment System](https://mlsanity.com/paper/2112.02682/) (2021)
- [Things not Written in Text: Exploring Spatial Commonsense from Visual Signals](https://mlsanity.com/paper/2203.08075/) (2022)
- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [The Unreasonable Effectiveness of Easy Training Data for Hard Tasks](https://mlsanity.com/paper/2401.06751/) (2024)
- [The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color](https://mlsanity.com/paper/2110.08182/) (2021)
- [FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery](https://mlsanity.com/paper/2211.08316/) (2022)
- [Can Language Models Understand Physical Concepts?](https://mlsanity.com/paper/2305.14057/) (2023)
- [Unified Semantic Typing with Meaningful Label Inference](https://mlsanity.com/paper/2205.01826/) (2022)
- [Sparse Pairwise Re-ranking with Pre-trained Transformers](https://mlsanity.com/paper/2207.04470/) (2022)
- [Do ever larger octopi still amplify reporting biases? Evidence from judgments of typical colour](https://mlsanity.com/paper/2209.12786/) (2022)
- [Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces](https://mlsanity.com/paper/2310.05481/) (2023)
- [A RelEntLess Benchmark for Modelling Graded Relations between Named Entities](https://mlsanity.com/paper/2305.15002/) (2023)

## Built on by (most cited first, 2 of 2)

- [Generative Product Recommendations for Implicit Superlative Queries](https://mlsanity.com/paper/2504.18748/) (2025)
- [Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities](https://mlsanity.com/paper/2510.16815/) (2025)

## Abstract (arXiv)

Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially when it comes to modelling perceptual and subjective features. Distilling conceptual spaces from Large Language Models (LLMs) has recently emerged as a promising strategy, but existing work has been limited to probing pre-trained LLMs using relatively simple zero-shot strategies. We focus in particular on the task of ranking entities according to a given conceptual space dimension. Unfortunately, we cannot directly fine-tune LLMs on this task, because ground truth rankings for conceptual space dimensions are rare. We therefore use more readily available features as training data and analyse whether the ranking capabilities of the resulting models transfer to perceptual and subjective features. We find that this is indeed the case, to some extent, but having at least some perceptual and subjective features in the training data seems essential for achieving the best results.