# Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives

arXiv: https://arxiv.org/abs/2603.29997 · HTML: https://mlsanity.com/paper/2603.29997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 9 of its 86 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 9 of 9)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Towards Fine-grained Causal Reasoning and QA](https://mlsanity.com/paper/2204.07408/) (2022)
- [Analogical Reasoning Inside Large Language Models: Concept Vectors and the Limits of Abstraction](https://mlsanity.com/paper/2503.03666/) (2025)
- [Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming](https://mlsanity.com/paper/2406.00554/) (2024)
- [Can Large Language Models generalize analogy solving like children can?](https://mlsanity.com/paper/2411.02348/) (2024)
- [Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research](https://mlsanity.com/paper/2509.09381/) (2025)
- [The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models](https://mlsanity.com/paper/2511.20344/) (2025)

## Built on by (most cited first, 1 of 1)

- [PRISM: A Category-Theoretic Framework for Measuring and Refining Multimodal Analogies](https://mlsanity.com/paper/2610.01383/) (2026)

## Abstract (arXiv)

Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cognitive engines for structural mapping are not directly applicable, as they assume pre-extracted entities, whereas LLMs' performance is sensitive to prompt format and the degree of surface similarity between narratives. This gap motivates a key question: What is the impact of enhancing structural mapping with LLM-derived abstractions on their analogical reasoning ability in narratives? To that end, we propose a modular framework named YARN (Yielding Abstractions for Reasoning in Narratives), which uses LLMs to decompose narratives into units, abstract these units, and then passes them to a mapping component that aligns elements across stories to perform analogical reasoning. We define and operationalize four levels of abstraction that capture both the general meaning of units and their roles in the story, grounded in prior work on framing. Our experiments reveal that abstractions consistently improve model performance, resulting in competitive or better performance than end-to-end LLM baselines. Closer error analysis reveals the remaining challenges in abstraction at the right level, in incorporating implicit causality, and an emerging categorization of analogical patterns in narratives. YARN enables systematic variation of experimental settings to analyze component contributions, and to support future work, we make the code for YARN openly available.