# An Interdisciplinary Review of Commonsense Reasoning and Intent Detection

arXiv: https://arxiv.org/abs/2506.14040 · HTML: https://mlsanity.com/paper/2506.14040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [Generated Knowledge Prompting for Commonsense Reasoning](https://mlsanity.com/paper/2110.08387/) (2021)
- [QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions](https://mlsanity.com/paper/1909.03553/) (2019)
- [SocialIQA: Commonsense Reasoning about Social Interactions](https://mlsanity.com/paper/1904.09728/) (2019)
- ["I'd rather just go to bed": Understanding Indirect Answers](https://mlsanity.com/paper/2010.03450/) (2020)
- [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://mlsanity.com/paper/1811.00207/) (2018)
- [Commonsense Reasoning for Conversational AI: A Survey of the State of the Art](https://mlsanity.com/paper/2302.07926/) (2023)

## Abstract (arXiv)

This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adaptation, structured evaluation, and interactive contexts. Intent detection is examined through open-set models, generative formulations, clustering, and human-centered systems. By bridging insights from NLP and HCI, we highlight emerging trends toward more adaptive, multilingual, and context-aware models, and identify key gaps in grounding, generalization, and benchmark design.