Past Meets Present: Creating Historical Analogy with Large Language Models (2025.acl-long)
Copied to clipboard
Nianqi Li, Siyu Yuan, Jiangjie Chen, Jiaqing Liang, Feng Wei, Zujie Liang, Deqing Yang, Yanghua Xiao
| Challenge: | Historical analogies are important abilities that help people make decisions and understand the world. |
| Approach: | They propose a historical analogy acquisition task that uses large language models to acquire historical analogies. |
| Outcome: | The proposed method mitigates hallucinations and stereotypes when LLMs generate historical analogies. |
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. |
Can Large Language Models Generalize Analogy Solving Like Children Can? (2026.tacl-1)
Copied to clipboard
| Challenge: | Recent research shows that large language models can solve various forms of analogies. |
| Approach: | They investigated whether large language models can generalize analogy solving to other domains . they found that LLMs struggle with robust human-like analogical transfer . |
| Outcome: | The results show that large language models can solve analogies in the Latin alphabet, Greek alphabet, and far transfer domains. |
Remember This Event That Year? Assessing Temporal Information and Understanding in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to effectively retain and reason about temporal information remains limited. |
| Approach: | They propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. |
| Outcome: | The proposed methods improve performance and reduce incorrect outputs. |
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. |
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. |
SimLLM: Detecting Sentences Generated by Large Language Models Using Similarity between the Generation and its Re-generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Prior studies have detected the generation of non-analogous text with substantial differences between original and generated content. |
| Approach: | They propose a method to detect analogous machine-generated sentences that closely mimic human-written ones by estimating the similarity between an input sentence and its generated counterpart. |
| Outcome: | The proposed approach outperforms existing methods in academic dishonesty, spam dissemination, and misinformation propagation. |
Analogy Models for Neural Word Inflection (2020.coling-main)
Copied to clipboard
| Challenge: | Neural network models are usually very data-hungry and performance of such models can suffer when labeled data is not available. |
| Approach: | They propose to provide models with additional analogy sources to strengthen analogy-formation . they propose to combine the analogy motivated approach with data hallucination or augmentation . |
| Outcome: | The proposed methods improve on state-of-the-art results on 46 languages, especially in low-resource settings. |
Tutorial Proposal: Hallucination in Large Language Models (2024.lrec-tutorials)
Copied to clipboard
| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
| Approach: | This tutorial aims to bridge the gap between the field and the field of hallucination . it will explore the key aspects of hallucinonation, including benchmarking, detection, and mitigation techniques . |
| Outcome: | This tutorial will explore the key aspects of hallucination in LLMs . it will also explore the specific constraints and shortcomings of current approaches . |
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. |