RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
arXiv preprint arXiv:1905.13164 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A BART-based hierarchical approach with golden-summary-driven document shortening achieves ROUGE2-F1 of 0.2468 on the VLSP 2022 Vietnamese multi-document summarization task and releases additional training data.
citing papers explorer
-
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
-
A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
A BART-based hierarchical approach with golden-summary-driven document shortening achieves ROUGE2-F1 of 0.2468 on the VLSP 2022 Vietnamese multi-document summarization task and releases additional training data.