# PaCo: Preconditions Attributed to Commonsense Knowledge

arXiv: https://arxiv.org/abs/2104.08712 · HTML: https://mlsanity.com/paper/2104.08712/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 58 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Annotation Artifacts in Natural Language Inference Data](https://mlsanity.com/paper/1803.02324/) (2018)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Abductive Commonsense Reasoning](https://mlsanity.com/paper/1908.05739/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Adversarial Filters of Dataset Biases](https://mlsanity.com/paper/2002.04108/) (2020)
- [Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering](https://mlsanity.com/paper/2005.00646/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [GLUCOSE: GeneraLized and COntextualized Story Explanations](https://mlsanity.com/paper/2009.07758/) (2020)
- [Inducing Relational Knowledge from BERT](https://mlsanity.com/paper/1911.12753/) (2019)
- [WIQA: A dataset for "What if..." reasoning over procedural text](https://mlsanity.com/paper/1909.04739/) (2019)
- [Reasoning about Goals, Steps, and Temporal Ordering with WikiHow](https://mlsanity.com/paper/2009.07690/) (2020)
- [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://mlsanity.com/paper/2010.03636/) (2020)
- [Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering](https://mlsanity.com/paper/1910.14087/) (2019)
- [Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources](https://mlsanity.com/paper/2103.11320/) (2021)
- [Dimensions of Commonsense Knowledge](https://mlsanity.com/paper/2101.04640/) (2021)
- [RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms](https://mlsanity.com/paper/2005.00782/) (2020)
- [Advanced Semantics for Commonsense Knowledge Extraction](https://mlsanity.com/paper/2011.00905/) (2020)

## Built on by (most cited first, 9 of 9)

- [Making Large Language Models into World Models with Precondition and Effect Knowledge](https://mlsanity.com/paper/2409.12278/) (2024)
- [PInKS: Preconditioned Commonsense Inference with Minimal Supervision](https://mlsanity.com/paper/2206.07920/) (2022)
- [VIPHY: Probing "Visible" Physical Commonsense Knowledge](https://mlsanity.com/paper/2209.07000/) (2022)
- [TEXT2AFFORD: Probing Object Affordance Prediction abilities of Language Models solely from Text](https://mlsanity.com/paper/2402.12881/) (2024)
- [What Really is Commonsense Knowledge?](https://mlsanity.com/paper/2411.03964/) (2024)
- [WinoViz: Probing Visual Properties of Objects Under Different States](https://mlsanity.com/paper/2402.13584/) (2024)
- [DiffG-RL: Leveraging Difference between State and Common Sense](https://mlsanity.com/paper/2211.16002/) (2022)
- [Preconditioned Visual Language Inference with Weak Supervision](https://mlsanity.com/paper/2306.01753/) (2023)
- [Playing with Voices: Tabletop Role-Playing Game Recordings as a Diarization Challenge](https://mlsanity.com/paper/2502.12714/) (2025)

## Abstract (arXiv)

Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the water is toxic. Despite state-of-the-art (SOTA) language models' (LMs) impressive performance on inferring commonsense knowledge, it is unclear whether they understand the circumstantial preconditions. To address this gap, we propose a novel challenge of reasoning with circumstantial preconditions. We collect a dataset, called PaCo, consisting of 12.4 thousand preconditions of commonsense statements expressed in natural language. Based on this dataset, we create three canonical evaluation tasks and use them to examine the capability of existing LMs to understand situational preconditions. Our results reveal a 10-30% gap between machine and human performance on our tasks, which shows that reasoning with preconditions is an open challenge.