ArticleNature communications2023
Augmenting interpretable models with large language models during training.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Large language models as versatile predictive engines for notifiable infectious diseases.PLOS digital health · 2026Article
- Advancing rheumatic disease care through generative artificial intelligence.Nature reviews. Rheumatology · 2026Article
- SafeTraffic Copilot: adapting large language models for trustworthy traffic safety assessments and decision interventions.Nature communications · 2025Article
- Nurse Researchers' Experiences and Perceptions of Generative AI: Qualitative Semistructured Interview Study.Journal of medical Internet research · 2025Article
- Evaluating scientific theories as predictive models in language neuroscience.bioRxiv : the preprint server for biology · 2025Article
- SnoBIRD: a tool to identify C/D box snoRNAs and refine their annotation across all eukaryotes.Nucleic acids research · 2025Article
- Performance analysis of large language models Chatgpt-4o, OpenAI O1, and OpenAI O3 mini in clinical treatment of pneumonia: a comparative study.Clinical and experimental medicine · 2025Article
- Model interpretability enhances domain generalization in the case of textual complexity modeling.Patterns (New York, N.Y.) · 2025Article
- Integration of large language models and federated learning.Patterns (New York, N.Y.) · 2024Review
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
- Crafting Interpretable Embeddings for Language Neuroscience by Asking LLMs Questions.Advances in neural information processing systems · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent large language models (LLMs), such as ChatGPT, have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains and compute-limited settings has created a burgeoning need for interpretability and efficiency. We address this need by proposing Aug-imodels, a framework for leveraging the knowledge learned by LLMs to build extremely efficient and interpretable prediction models. Aug-imodels use LLMs during fitting but not during inference, allowing complete transparency and often a speed/memory improvement of greater than 1000x for inference compared to LLMs. We explore two instantiations of Aug-imodels in natural-language processing: Aug-Linear, which augments a linear model with decoupled embeddings from an LLM and Aug-Tree, which augments a decision tree with LLM feature expansions. Across a variety of text-classification datasets, both outperform their non-augmented, interpretable counterparts. Aug-Linear can even outperform much larger models, e.g. a 6-billion parameter GPT-J model, despite having 10,000x fewer parameters and being fully transparent. We further explore Aug-imodels in a natural-language fMRI study, where they generate interesting interpretations from scientific data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.