# Reasoning about Goals, Steps, and Temporal Ordering with WikiHow

arXiv: https://arxiv.org/abs/2009.07690 · HTML: https://mlsanity.com/paper/2009.07690/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 66)

- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [CLadder: Assessing Causal Reasoning in Language Models](https://mlsanity.com/paper/2312.04350/) (2023)
- [Natural Language Reasoning, A Survey](https://mlsanity.com/paper/2303.14725/) (2023)
- [Procedure-Aware Pretraining for Instructional Video Understanding](https://mlsanity.com/paper/2303.18230/) (2023)
- [Visual Goal-Step Inference using wikiHow](https://mlsanity.com/paper/2104.05845/) (2021)
- [Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scale](https://mlsanity.com/paper/2409.15637/) (2024)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Multimodal Procedural Planning via Dual Text-Image Prompting](https://mlsanity.com/paper/2305.01795/) (2023)
- [Goal-Oriented Script Construction](https://mlsanity.com/paper/2107.13189/) (2021)
- [Neuro-Symbolic Procedural Planning with Commonsense Prompting](https://mlsanity.com/paper/2206.02928/) (2022)
- [SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos](https://mlsanity.com/paper/2403.01599/) (2024)
- [Causal Reasoning of Entities and Events in Procedural Texts](https://mlsanity.com/paper/2301.10896/) (2023)
- [Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data](https://mlsanity.com/paper/2203.07264/) (2022)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification](https://mlsanity.com/paper/2307.01972/) (2023)
- [Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals](https://mlsanity.com/paper/2110.08486/) (2021)
- [UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization](https://mlsanity.com/paper/2407.03525/) (2024)
- [A Critical Review of Causal Reasoning Benchmarks for Large Language Models](https://mlsanity.com/paper/2407.08029/) (2024)
- [Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval](https://mlsanity.com/paper/2111.09276/) (2021)
- [Multimedia Generative Script Learning for Task Planning](https://mlsanity.com/paper/2208.12306/) (2022)
- [TaskLAMA: Probing the Complex Task Understanding of Language Models](https://mlsanity.com/paper/2308.15299/) (2023)
- [CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans](https://mlsanity.com/paper/2406.15823/) (2024)
- [Coalescing Global and Local Information for Procedural Text Understanding](https://mlsanity.com/paper/2208.12848/) (2022)
- [SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller](https://mlsanity.com/paper/2210.06694/) (2022)
- [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://mlsanity.com/paper/2308.00002/) (2023)
- [CELLO: Causal Evaluation of Large Vision-Language Models](https://mlsanity.com/paper/2406.19131/) (2024)
- [PaCo: Preconditions Attributed to Commonsense Knowledge](https://mlsanity.com/paper/2104.08712/) (2021)
- [Zero-Shot On-the-Fly Event Schema Induction](https://mlsanity.com/paper/2210.06254/) (2022)
- [What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation](https://mlsanity.com/paper/2201.07099/) (2022)
- [Reasoning about Procedures with Natural Language Processing: A Tutorial](https://mlsanity.com/paper/2205.07455/) (2022)

## Abstract (arXiv)

We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses"). We introduce a dataset targeting these two relations based on wikiHow, a website of instructional how-to articles. Our human-validated test set serves as a reliable benchmark for commonsense inference, with a gap of about 10% to 20% between the performance of state-of-the-art transformer models and human performance. Our automatically-generated training set allows models to effectively transfer to out-of-domain tasks requiring knowledge of procedural events, with greatly improved performances on SWAG, Snips, and the Story Cloze Test in zero- and few-shot settings.