mlsanityData to 7 Oct 2026

How Large Are Lions? Inducing Distributions over Quantitative Attributes

arXiv 1906.01327 ↗First posted 4 Jun 2019cs.CLcited by 26

Not on the map. Why?

Abstract (arXiv)

Most current NLP systems have little knowledge about quantitative attributes of objects and events. We propose an unsupervised method for collecting quantitative information from large amounts of web data, and use it to create a new, very large resource consisting of distributions over physical quantities associated with objects, adjectives, and verbs which we call Distributions over Quantitative (DoQ). This contrasts with recent work in this area which has focused on making only relative comparisons such as "Is a lion bigger than a wolf?". Our evaluation shows that DoQ compares favorably with state of the art results on existing datasets for relative comparisons of nouns and adjectives, and on a new dataset we introduce.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 2; 2023: 9; 2024: 4; 2025: 2; 2026: 1.

26 papers.

Sort:

By area:

  1. 2021Representing Numbers in NLP: a Survey and a Visioncited by 83
  2. 2024Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Datacited by 83
  3. 2021RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledgecited by 60
  4. 2021PROST: Physical Reasoning of Objects through Space and Timecited by 48
  5. 2022Things not Written in Text: Exploring Spatial Commonsense from Visual Signalscited by 41
  6. 2021Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schemacited by 32
  7. 2023Mathematical Language Models: A Surveycited by 24
  8. 2023Mitigating Temporal Misalignment by Discarding Outdated Factscited by 21
  9. 2021Refined Commonsense Knowledge from Large-Scale Web Contentscited by 19
  10. 2024SEC-QA: A Systematic Evaluation Corpus for Financial QAcited by 17
  11. 2025A Survey on Mathematical Reasoning and Optimization with Large Language Modelscited by 12
  12. 2021One Billion Audio Sounds from GPU-enabled Modular Synthesiscited by 11
14 more
  1. 2023NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledgecited by 8
  2. 2023Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Featurescited by 6
  3. 2023Towards Concept-Aware Large Language Modelscited by 6
  4. 2021Masked Measurement Prediction: Learning to Jointly Predict Quantities and Units from Textual Contextcited by 5
  5. 2023Probing Physical Reasoning with Counter-Commonsense Contextcited by 4
  6. 2023Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perceptioncited by 4
  7. 2022Do Language Models Understand Measurements?cited by 2
  8. 2023KitchenScale: Learning to predict ingredient quantities from recipe contextscited by 2
  9. 2021Inferring spatial relations from textual descriptions of imagescited by 1
  10. 2023Class Cardinality Comparison as a Fermi Problemcited by 1
  11. 2024Grounding Spatial Relations in Text-Only Language Modelscited by 1
  12. 2026Debias in Text, Believe Your Eyes: Text-Anchored Cross-Modal Transfer for Visual Counter-Commonsense Reasoningcited by 1
  13. 2024NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Modelscited by 0
  14. 2025TComQA: Extracting Temporal Commonsense from Textcited by 0