LLMs can construct powerful representations and streamline sample-efficient supervised learning

• URL: http://arxiv.org/abs/2603.11679v1
• Date: Thu, 12 Mar 2026 08:44:06 GMT
• Title: LLMs can construct powerful representations and streamline sample-efficient supervised learning
• Abstract summary: We propose an agentic pipeline to streamline the process of modeling multimodal data for downstream tasks.<n>Our approaches significantly outperform traditional count-feature models, naive text-serialization-based baselines, and a clinical foundation model.
• Score: 3.5741706722720377
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific engineering. We propose an agentic pipeline to streamline this process. First, an LLM analyzes a small but diverse subset of text-serialized input examples in-context to synthesize a global rubric, which acts as a programmatic specification for extracting and organizing evidence. This rubric is then used to transform naive text-serializations of inputs into a more standardized format for downstream models. We also describe local rubrics, which are task-conditioned summaries generated by an LLM. Across 15 clinical tasks from the EHRSHOT benchmark, our rubric-based approaches significantly outperform traditional count-feature models, naive text-serialization-based LLM baselines, and a clinical foundation model, which is pretrained on orders of magnitude more data. Beyond performance, rubrics offer several advantages for operational healthcare settings such as being easy to audit, cost-effectiveness to deploy at scale, and they can be converted to tabular representations that unlock a swath of machine learning techniques.

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