mlsanityData to 7 Oct 2026

ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding

arXiv 2109.06838 ↗First posted 14 Sep 2021cs.CLcited by 14

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Abstract (arXiv)

While large language models have shown exciting progress on several NLP benchmarks, evaluating their ability for complex analogical reasoning remains under-explored. Here, we introduce a high-quality crowdsourced dataset of narratives for employing proverbs in context as a benchmark for abstract language understanding. The dataset provides fine-grained annotation of aligned spans between proverbs and narratives, and contains minimal lexical overlaps between narratives and proverbs, ensuring that models need to go beyond surface-level reasoning to succeed. We explore three tasks: (1) proverb recommendation and alignment prediction, (2) narrative generation for a given proverb and topic, and (3) identifying narratives with similar motifs. Our experiments show that neural language models struggle on these tasks compared to humans, and these tasks pose multiple learning challenges.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 28 of its 67 references that are in the map. First posted on arXiv 14 Sep 2021.

Builds on

28 of 67 references.

References · 28

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16 more
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Citing papers per year: 2023: 4; 2024: 2; 2025: 3; 2026: 5.

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