mlsanityData to 7 Oct 2026

Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation

arXiv 2412.15375 ↗First posted 19 Dec 2024cs.CLcited by 5

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Extracting metaphors and analogies from free text requires high-level reasoning abilities such as abstraction and language understanding. Our study focuses on the extraction of the concepts that form metaphoric analogies in literary texts. To this end, we construct a novel dataset in this domain with the help of domain experts. We compare the out-of-the-box ability of recent large language models (LLMs) to structure metaphoric mappings from fragments of texts containing proportional analogies. The models are further evaluated on the generation of implicit elements of the analogy, which are indirectly suggested in the texts and inferred by human readers. The competitive results obtained by LLMs in our experiments are encouraging and open up new avenues such as automatically extracting analogies and metaphors from text instead of investing resources in domain experts to manually label data.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 12 of its 35 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

12 of 35 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Metaphor Understanding Challenge Dataset for LLMs18 Mar
  2. 2023Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?2 Aug

Other references · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  6. 2021Abstraction and Analogy-Making in Artificial Intelligencecited by 103
  7. 2021BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?cited by 56
  8. 2021Metaphor Generation with Conceptual Mappingscited by 32
  9. 2022Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processescited by 22
  10. 2023Do large language models solve verbal analogies like children do?cited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025The Reader is the Metric: How Textual Features and Reader Profiles Explain Conflicting Evaluations of AI Creative Writingcited by 7
  2. 2026Computational Hermeneutics: Evaluating generative AI as a cultural technologycited by 7
  3. 2025From Polyester Girlfriends to Blind Mice: Creating the First Pragmatics Understanding Benchmarks for Slovenecited by 0
  4. 2026Towards High-Level Semantic Intelligencecited by 0
  5. 2026On the Diversity of Analogy Making in Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing multimodal analogical reasoning with Logic Augmented Generation
  2. 2024Science is Exploration: Computational Frontiers for Conceptual Metaphor Theory
  3. 2025Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
  4. 2025Modeling Understanding of Story-Based Analogies Using Large Language Models
  5. 2026PRISM: A Category-Theoretic Framework for Measuring and Refining Multimodal Analogies
  6. 2024A Dual-Perspective Metaphor Detection Framework Using Large Language Models
  7. 2026Metaphors are a Source of Cross-Domain Misalignment of Large Reasoning Models
  8. 2023ARN: Analogical Reasoning on Narratives