# Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes arXiv: https://arxiv.org/abs/2210.12197 ยท HTML: https://mlsanity.com/paper/2210.12197/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 40 references that are in the map. - First posted on arXiv 21 Oct 2022. ## Builds on (most cited first, 18 of 18) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021) - [Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension](https://mlsanity.com/paper/1805.06975/) (2018) - [BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?](https://mlsanity.com/paper/2105.04949/) (2021) - [Simulating Action Dynamics with Neural Process Networks](https://mlsanity.com/paper/1711.05313/) (2017) - [Tracking the World State with Recurrent Entity Networks](https://mlsanity.com/paper/1612.03969/) (2016) - [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018) - [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021) - [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://mlsanity.com/paper/1911.03243/) (2019) - [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017) - [Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text](https://mlsanity.com/paper/1909.04745/) (2019) - [Reasoning about Actions and State Changes by Injecting Commonsense Knowledge](https://mlsanity.com/paper/1808.10012/) (2018) - [Issues in evaluating semantic spaces using word analogies](https://mlsanity.com/paper/1606.07736/) (2016) - [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://mlsanity.com/paper/2109.12655/) (2021) - [Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval](https://mlsanity.com/paper/2102.11127/) (2021) - [Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching](https://mlsanity.com/paper/2010.07668/) (2020) ## Built on by (most cited first, 22 of 22) - [Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models](https://mlsanity.com/paper/2406.04271/) (2024) - [Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models](https://mlsanity.com/paper/2310.03965/) (2023) - [StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding](https://mlsanity.com/paper/2310.12874/) (2023) - [In-Context Analogical Reasoning with Pre-Trained Language Models](https://mlsanity.com/paper/2305.17626/) (2023) - [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024) - [Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction](https://mlsanity.com/paper/2305.12660/) (2023) - [ARN: Analogical Reasoning on Narratives](https://mlsanity.com/paper/2310.00996/) (2023) - [ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies](https://mlsanity.com/paper/2403.01139/) (2024) - [QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization](https://mlsanity.com/paper/2412.07096/) (2024) - [Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://mlsanity.com/paper/2306.16296/) (2023) - [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024) - [Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation](https://mlsanity.com/paper/2412.15375/) (2024) - [FAME: Flexible, Scalable Analogy Mappings Engine](https://mlsanity.com/paper/2311.01860/) (2023) - [Boosting Scientific Concepts Understanding: Can Analogy from Teacher Models Empower Student Models?](https://mlsanity.com/paper/2406.11375/) (2024) - [RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation](https://mlsanity.com/paper/2409.16383/) (2024) - [RelBERT: Embedding Relations with Language Models](https://mlsanity.com/paper/2310.00299/) (2023) - [On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models](https://mlsanity.com/paper/2310.07818/) (2023) - [Past Meets Present: Creating Historical Analogy with Large Language Models](https://mlsanity.com/paper/2409.14820/) (2024) - [KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting](https://mlsanity.com/paper/2412.00869/) (2024) - [Effective QA-driven Annotation of Predicate-Argument Relations Across Languages](https://mlsanity.com/paper/2602.22865/) (2026) - [Solving Hard Analogy Questions with Relation Embedding Chains](https://mlsanity.com/paper/2310.12379/) (2023) - [FrameNet Semantic Role Classification by Analogy](https://mlsanity.com/paper/2603.19825/) (2026) ## Abstract (arXiv) Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference -- abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community, with most research focusing on simple word analogies. Work that tackled more complex analogies relied heavily on manually constructed, hard-to-scale input representations. In this work, we explore a more realistic, challenging setup: our input is a pair of natural language procedural texts, describing a situation or a process (e.g., how the heart works/how a pump works). Our goal is to automatically extract entities and their relations from the text and find a mapping between the different domains based on relational similarity (e.g., blood is mapped to water). We develop an interpretable, scalable algorithm and demonstrate that it identifies the correct mappings 87% of the time for procedural texts and 94% for stories from cognitive-psychology literature. We show it can extract analogies from a large dataset of procedural texts, achieving 79% precision (analogy prevalence in data: 3%). Lastly, we demonstrate that our algorithm is robust to paraphrasing the input texts.