arXiv — NLP / Computation & Language · · 3 min read

HiVe: Beyond Static Prompts for Multitask Learning via Hierarchy-based Vertical Mixture-of-Experts

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Computer Science > Computation and Language

arXiv:2608.29790 (cs)
[Submitted on 30 Aug 2026]

Title:HiVe: Beyond Static Prompts for Multitask Learning via Hierarchy-based Vertical Mixture-of-Experts

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Abstract:As large language models (LLMs) continue to scale, parameter-efficient fine-tuning (PEFT) has become a practical alternative to full-parameter adaptation. Prompt tuning is effective, but existing approaches either use flat prompt structures or hierarchical structures with fixed prompt composition, limiting adaptive prompt specialization. To address this limitation, we propose HiVe, a prompt tuning framework that models prompts at multiple levels and enables input-dependent specialization. HiVe constructs a prompt hierarchy by leveraging inter-task relationships during training, and employs a vertical mixture-of-experts (V-MoE) mechanism at inference time to compose prompts up to the level of specialization required for each input. Experiments show that HiVe consistently outperforms strong prompt tuning baselines across diverse tasks.
Comments: 16 pages, 6 figures. Accepted to the EMNLP 2026 Main Conference
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.29790 [cs.CL]
  (or arXiv:2608.29790v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.29790
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: HyeonJik Bae [view email]
[v1] Sun, 30 Aug 2026 13:35:42 UTC (212 KB)
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