# Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs

arXiv: https://arxiv.org/abs/2303.12810 · HTML: https://mlsanity.com/paper/2303.12810/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [SpartQA: : A Textual Question Answering Benchmark for Spatial Reasoning](https://mlsanity.com/paper/2104.05832/) (2021)

## Built on by (most cited first, 7 of 7)

- [LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planning](https://mlsanity.com/paper/2407.02511/) (2024)
- [Understanding the Capabilities of Large Language Models for Automated Planning](https://mlsanity.com/paper/2305.16151/) (2023)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness](https://mlsanity.com/paper/2401.15127/) (2024)
- [Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research](https://mlsanity.com/paper/2503.05770/) (2025)
- [PsychoLex: Unveiling the Psychological Mind of Large Language Models](https://mlsanity.com/paper/2408.08848/) (2024)
- [Generative Artificial Intelligence for Literature Reviews](https://mlsanity.com/paper/2605.16475/) (2026)

## Abstract (arXiv)

The potential of large language models (LLMs) to reason like humans has been a highly contested topic in Machine Learning communities. However, the reasoning abilities of humans are multifaceted and can be seen in various forms, including analogical, spatial and moral reasoning, among others. This fact raises the question whether LLMs can perform equally well across all these different domains. This research work aims to investigate the performance of LLMs on different reasoning tasks by conducting experiments that directly use or draw inspirations from existing datasets on analogical and spatial reasoning. Additionally, to evaluate the ability of LLMs to reason like human, their performance is evaluted on more open-ended, natural language questions. My findings indicate that LLMs excel at analogical and moral reasoning, yet struggle to perform as proficiently on spatial reasoning tasks. I believe these experiments are crucial for informing the future development of LLMs, particularly in contexts that require diverse reasoning proficiencies. By shedding light on the reasoning abilities of LLMs, this study aims to push forward our understanding of how they can better emulate the cognitive abilities of humans.