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.

Large language models (LLMs) are increasingly adopted for handling structured data, including tabular and relational inputs, despite mostly being pretrained on unstructured text. This raises a key question: how effectively do pretrained representations from language-focused LLMs transfer to tasks involving structured inputs? We address this through controlled experiments using two small open-source LLMs, systematically re-initializing subsets of layers with random weights before fine-tuning on structured datasets and comparing results to unstructured datasets. Our analyses show that, for structured data, most pretrained depth contributes little, with performance often saturating after the first few layers, whereas unstructured tasks benefit more consistently from deeper pretrained representations. Pretraining remains useful mainly in low-resource settings, with its impact diminishing as more training data becomes available.

Predictive modeling of hospital patient data is challenging due to its structured format, irregular timing of measurements, and variation in data representation across institutions. While traditional models often struggle with such inconsistencies, Large Language Models (LLMs) offer a flexible alternative. In this work, we propose a method for verbalizing structured Electronic Health Records (EHRs) into a format suitable for LLMs and systematically examine how to include time-stamped clinical observations—such as lab tests and vital signs—from previous time points in the prompt. We study how different ways of structuring this temporal information affect predictive performance, and whether fine-tuning alone enables LLMs to effectively reason over such data. Evaluated on two real-world hospital datasets and MIMIC-IV, our approach achieves strong in-hospital and cross-hospital performance, laying the groundwork for more generalizable clinical modeling.