StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding (2023.emnlp-main)
Copied to clipboard
Cheng Jiayang, Lin Qiu, Tsz Chan, Tianqing Fang, Weiqi Wang, Chunkit Chan, Dongyu Ru, Qipeng Guo, Hongming Zhang, Yangqiu Song, Yue Zhang, Zheng Zhang
| Challenge: | Analogy-making between narratives is crucial for human reasoning . despite its importance, there has been limited research on story analogies . |
| Approach: | They construct a large-scale story-level analogy corpus with 24K story pairs . they find that the tasks are incredibly difficult for large language models such as ChatGPT . |
| Outcome: | The proposed corpus contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. |
Similar Papers
AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies (2024.emnlp-main)
Copied to clipboard
Xiao Ye, Andrew Wang, Jacob Choi, Yining Lu, Shreya Sharma, Lingfeng Shen, Vijay Murari Tiyyala, Nicholas Andrews, Daniel Khashabi
| Challenge: | Analogical reasoning is an important part of human communication, says a new study . a benchmark to determine analogical reasoning ability in language models is needed . |
| Approach: | They propose to benchmark analogical reasoning ability in language models by collecting 340 analogies from human writings. |
| Outcome: | The proposed benchmark aims to determine analogical reasoning ability in language models. |
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models (2024.findings-eacl)
Copied to clipboard
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Analogies facilitate the transfer of meaning and knowledge from one domain to another. |
| Approach: | They propose to use large language models to encode syntactic and semantic structures of sentences to identify sentence analogies. |
| Outcome: | The LLMs which capture syntactic structures better, also have higher abilities in identifying sentence analogies. |
ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base (2024.acl-long)
Copied to clipboard
| Challenge: | ANALOGYKB is a million-scale analogy knowledge base based on existing knowledge graphs (KGs) based upon relational knowledge triples, we can discover new analogies using the corresponding relations between concepts. |
| Approach: | They propose a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) ANALOGYKB identifies analogies of the same relations and analogies from analogous relations . |
| Outcome: | The proposed model enables both smaller LMs and LLMs to gain better analogical reasoning capabilities. |
Can Language Models Serve as Analogy Annotators? (2025.findings-acl)
Copied to clipboard
| Challenge: | Conceptual abstraction and analogy-making are crucial for human learning, reasoning, and adapting to unfamiliar domains. |
| Approach: | They propose a multi-stage progressive reasoning prompt framework A3E which is based on the structure mapping theory from cognitive psychology and efficiently annotates candidate story pairs across six fine-grained categories. |
| Outcome: | The proposed framework achieves an average performance gain of + 73% across a range of prompting baselines and base LLMs. |
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models (2023.findings-acl)
Copied to clipboard
Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal Gajera, Shreeyash Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das
| Challenge: | Modern large language models are evaluated on extrinsic measures based on benchmarks such as GLUE and SuperGLUE. |
| Approach: | They propose a benchmark to intrinsically evaluate large language models across a taxonomy of analogies of long text with six levels of complexity. |
| Outcome: | The proposed benchmark evaluates LLMs across a taxonomy of analogies of long text with six levels of complexity. |
Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
Copied to clipboard
Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
| Outcome: | The proposed models can extract metaphors from literary texts without using domain experts. |
Beneath Surface Similarity: Large Language Models Make Reasonable Scientific Analogies after Structure Abduction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have focused on word analogies, but they neglect structures that underpin analogical reasoning. |
| Approach: | They propose a task to abduct structures that form an analogy between two systems to evaluate their analogical reasoning abilities. |
| Outcome: | The proposed task is based on 400 scientific analogies from 13 different fields and is compared with a standard SCAR benchmark. |
Scientific and Creative Analogies in Pretrained Language Models (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing analogy datasets focus on a limited set of analogical relations with a high similarity of the two domains between which the analogy holds. |
| Approach: | They propose a dataset that encodes analogy in pretrained language models . they use a system that maps attributes and relational structures across dissimilar domains . |
| Outcome: | The proposed dataset shows that state-of-the-art models achieve low performance on analogy tasks . |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
Copied to clipboard
Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Are Large Language Models Capable of Generating Human-Level Narratives? (2024.emnlp-main)
Copied to clipboard
Yufei Tian, Tenghao Huang, Miri Liu, Derek Jiang, Alexander Spangher, Muhao Chen, Jonathan May, Nanyun Peng
| Challenge: | a recent HCI study has pointed to gaps in machine storytelling ability at the global level . authors show that LLMs have less suspense and less tension than human stories . |
| Approach: | They propose a computational framework to analyze narratives through three discourse-level aspects. |
| Outcome: | The proposed framework analyzes narratives through three discourse-level aspects . it shows that LLMs fall short of human abilities in discourse understanding . |