Pith. sign in

REVIEW 1 cited by

Structured Prompt Tuning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2205.12309 v1 pith:2RDFGCKK submitted 2022-05-24 cs.CL

Chi-Liang Liu , Hung-yi Lee , Wen-tau Yih This is my paper · ORCID
classification cs.CL
keywords prompttuningstructuredembeddingsstandardallowsappliedapproach
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

We propose structured prompt tuning, a simple and effective method to improve prompt tuning. Instead of prepending a sequence of tunable embeddings to the input, we generate the soft prompt embeddings through a hypernetwork. Our approach subsumes the standard prompt tuning, allows more flexibility in model design and can be applied to both single-task and multi-task training settings. Empirically, structured prompt tuning shows a gain of +1.2$~1.5 points on the GLUE benchmark and is less sensitive to the change of learning rate, compared to standard prompt tuning.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ViseGPT: Towards Better Alignment of LLM-generated Data Wrangling Scripts and User Prompts

    cs.HC 2025-08 conditional novelty 6.0 of 10

    ViseGPT automatically converts user prompts into test cases and visualizes which steps of an LLM-generated data wrangling script pass or fail.

Pith tools