A fable is a short narrative whose purpose is to convey a moral lesson. While semantically related, morals and fables differ fundamentally in form: morals are abstract and propositional, while fables express the same idea through characters and plot. In this work, we ask how well this conceptual relationship is captured in the latent representations of embedding models. To study this, we introduce MoraLink, a benchmark for moral-to-fable retrieval in which models must retrieve the correct fable for a given moral. We find that off-the-shelf embedding models struggle with this task, and that augmenting fable documents with LLM-generated moral-style summaries substantially improves retrieval, identifying document representation as the primary bottleneck. Contrastive fine-tuning further improves performance, and we release MoraLink as a challenging testbed for probing how well embedding models capture the abstraction gap between a moral principle and the narrative that embodies it.

Large language models (LLMs) achieve strong performance on idiom identification benchmarks, yet their robustness to misleading contextual signals remains largely untested. We introduce ID10M-JAM, an adversarial extension of the ID10M dataset designed to jam model understanding by injecting coherent but conflicting context before each target sentence. For every sentence containing a potential idiomatic expression (PIE), we construct variants that deliberately invert contextual expectations: placing literal cues before idiomatic uses and idiomatic cues before literal ones. All variants are validated by human annotators to ensure naturalness and unambiguous interpretation for human readers. ID10M-JAM exposes systematic vulnerabilities in LLMs’ contextual reasoning, pushing idiom identification to its breaking point.

Idioms pose a fundamental challenge for language models, as their meaning cannot be inferred from surface form alone. Understanding such expressions, therefore, requires semantic abstraction beyond lexical overlap. We introduce IdioLink, a retrieval benchmark designed to test whether models can link idiomatic expressions to conceptually equivalent meanings expressed in literal or paraphrased forms. IdioLink comprises 10,700 documents and 2,140 queries, spanning 107 idioms with both literal and figurative uses. Each document and query is annotated with spans that convey the core meaning. Evaluating strong embedding baselines (e.g., BGE, E5, Contriever, and Qwen), we show that current models struggle to retrieve equivalent meanings across divergent surface realizations, relying instead on topical and shallow semantic cues. IdioLink exposes key gaps in idiom-aware semantic retrieval and provides a challenging testbed for future models.