# Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare

arXiv: https://arxiv.org/abs/2607.17508 · HTML: https://mlsanity.com/paper/2607.17508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 26 of its 54 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Pre-Trained Language Models for Interactive Decision-Making](https://mlsanity.com/paper/2202.01771/) (2022)
- [Guiding Pretraining in Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2302.06692/) (2023)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions](https://mlsanity.com/paper/1506.00511/) (2015)
- [Causal Discovery with Language Models as Imperfect Experts](https://mlsanity.com/paper/2307.02390/) (2023)
- [MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records](https://mlsanity.com/paper/1905.03218/) (2019)
- [Efficient Reinforcement Learning with Large Language Model Priors](https://mlsanity.com/paper/2410.07927/) (2024)
- [Zero-Shot Robustification of Zero-Shot Models](https://mlsanity.com/paper/2309.04344/) (2023)
- [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://mlsanity.com/paper/2402.00746/) (2024)
- [AdaRefiner: Refining Decisions of Language Models with Adaptive Feedback](https://mlsanity.com/paper/2309.17176/) (2023)
- [PRISM: Enhancing Protein Inverse Folding through Fine-Grained Retrieval on Structure-Sequence Multimodal Representations](https://mlsanity.com/paper/2510.11750/) (2025)
- [Learning Sample-Specific Models with Low-Rank Personalized Regression](https://mlsanity.com/paper/1910.06939/) (2019)
- [LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying](https://mlsanity.com/paper/2308.13542/) (2023)
- [Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning](https://mlsanity.com/paper/2310.07918/) (2023)
- [Contextualized Machine Learning](https://mlsanity.com/paper/2310.11340/) (2023)
- [BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task](https://mlsanity.com/paper/2202.13174/) (2022)

## Built on by (most cited first, 1 of 1)

- [FACET: Factorized Asymmetric Conditioning for Efficient Transport in High-Fidelity Fluorescence Microscopy Synthesis](https://mlsanity.com/paper/2610.05353/) (2026)

## Abstract (arXiv)

We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors. RAIL retrieves related source tasks, transfers structure through coefficient space, and generates a new predictor in the original diagnostic-feature space, enabling zero-shot and few-shot clinical procedure prediction with feature-level explanations. Its probabilistic formulation provides uncertainty over retrieval, model coefficients, and predictions, supporting uncertainty-aware deployment: uncertain predictions or unstable explanations can be flagged for additional clinical review rather than treated as automatic decisions. This makes RAIL particularly suited for healthcare settings, where prediction tasks are highly long-tailed, new clinical targets arise frequently, and models must remain inspectable, uncertainty-aware, and compatible with human oversight. Across long-tailed clinical procedure prediction tasks, RAIL improves low-data model generation, benefits from clinically informed task representations, and yields retrieval, uncertainty, and coefficient-level diagnostics that make model behavior more transparent. These results suggest a path toward scalable clinical prediction systems that can adapt to new tasks while preserving interpretability and reliability.