On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions
Not on the map. Why?
Abstract (arXiv)
Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning. Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamiliar or novel situations. This enhances models' inferential capabilities and supports the effective transfer of knowledge across various domains. Despite its significance, the broad nature of this term has led to inconsistencies in understanding conceptualization across various works, as there exists different types of instances that can be abstracted in a wide variety of ways. There is also a lack of a systematic overview that comprehensively examines existing works on the definition, execution, and application of conceptualization to enhance reasoning tasks. In this paper, we address these gaps by first proposing a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized, in order to clarify the term and define the scope of our work. Then, we present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels. Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 82 of its 172 references that are in the map. First posted on arXiv 16 Jun 2024.
Builds on
82 of 172 references.
References · 82
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2020Measuring Massive Multitask Language Understandingcited by 7,768
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
- 2016Pointer Sentinel Mixture Modelscited by 2,992
- 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
38 more
- 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
- 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
- 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
- 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
- 2023Is ChatGPT a General-Purpose Natural Language Processing Task Solver?cited by 486
- 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
- 2019Abductive Commonsense Reasoningcited by 300
- 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
- 2020COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphscited by 254
- 2021A Dataset for Answering Time-Sensitive Questionscited by 150
- 2020CausaLM: Causal Model Explanation Through Counterfactual Language Modelscited by 117
- 2017The Consciousness Priorcited by 105
- 2019ASER: A Large-scale Eventuality Knowledge Graphcited by 89
- 2018Ultra-Fine Entity Typingcited by 87
- 2019GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledgecited by 78
- 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
- 2019CLOSURE: Assessing Systematic Generalization of CLEVR Modelscited by 63
- 2023ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Modelscited by 63
- 2021Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarizationcited by 50
- 2024NegotiationToM: A Benchmark for Stress-testing Machine Theory of Mind on Negotiation Surroundingcited by 49
- 2023NEWTON: Are Large Language Models Capable of Physical Reasoning?cited by 45
- 2019KBQA: Learning Question Answering over QA Corpora and Knowledge Basescited by 44
- 2023Vera: A General-Purpose Plausibility Estimation Model for Commonsense Statementscited by 44
- 2022Controllable Text Generation with Neurally-Decomposed Oraclecited by 41
- 2021Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Modelcited by 39
- 2021Modeling Fine-Grained Entity Types with Box Embeddingscited by 36
- 2021PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoningcited by 34
- 2023Benchmarking Large Language Model Capabilities for Conditional Generationcited by 29
- 2021ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualitiescited by 28
- 2023Logical Message Passing Networks with One-hop Inference on Atomic Formulascited by 28
- 2023CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answeringcited by 28
- 2024CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoningcited by 28
- 2021DISCOS: Bridging the Gap between Discourse Knowledge and Commonsense Knowledgecited by 26
- 2023StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understandingcited by 26
- 2024Benchmarking Large Language Models on Controllable Generation under Diversified Instructionscited by 26
- 2023CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoningcited by 25
- 2023It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performancecited by 25
50 most cited of 82 All 82 (JSON)
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2025One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMscited by 12
- 2026HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learningcited by 4
- 2024ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoningcited by 2
- 2026HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning30 Jan 2026
- 2025One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs12 Feb 2025
- 2024ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning16 Dec 2024