# Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning

arXiv: https://arxiv.org/abs/2310.16731 · HTML: https://mlsanity.com/paper/2310.16731/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 10 of 10)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Recurrent Relational Networks](https://mlsanity.com/paper/1711.08028/) (2017)
- [Multi-hop Question Answering](https://mlsanity.com/paper/2204.09140/) (2022)
- [Large Language Models are Strong Zero-Shot Retriever](https://mlsanity.com/paper/2304.14233/) (2023)
- [ThinkSum: Probabilistic reasoning over sets using large language models](https://mlsanity.com/paper/2210.01293/) (2022)
- [End-To-End Memory Networks](https://mlsanity.com/paper/1503.08895/) (2015)
- [Relational Gating for "What If" Reasoning](https://mlsanity.com/paper/2105.13449/) (2021)
- [The Role of Semantic Parsing in Understanding Procedural Text](https://mlsanity.com/paper/2302.06829/) (2023)

## Built on by (most cited first, 6 of 6)

- [SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models](https://mlsanity.com/paper/2406.04566/) (2024)
- [Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language](https://mlsanity.com/paper/2407.20513/) (2024)
- [An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning](https://mlsanity.com/paper/2503.05439/) (2025)
- [Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis](https://mlsanity.com/paper/2509.07122/) (2025)
- [AS-ES Learning: Towards Efficient CoT Learning in Small Models](https://mlsanity.com/paper/2403.01969/) (2024)
- [Spatial Reasoning via Modality Switching Between Language and Symbolic Representations](https://mlsanity.com/paper/2606.31285/) (2026)

## Abstract (arXiv)

Spatial reasoning over text is challenging as the models not only need to extract the direct spatial information from the text but also reason over those and infer implicit spatial relations. Recent studies highlight the struggles even large language models encounter when it comes to performing spatial reasoning over text. In this paper, we explore the potential benefits of disentangling the processes of information extraction and reasoning in models to address this challenge. To explore this, we design various models that disentangle extraction and reasoning(either symbolic or neural) and compare them with state-of-the-art(SOTA) baselines with no explicit design for these parts. Our experimental results consistently demonstrate the efficacy of disentangling, showcasing its ability to enhance models' generalizability within realistic data domains.