ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2112.05717
24
6

Discourse-Aware Soft Prompting for Text Generation

10 December 2021
Marjan Ghazvininejad
Vladimir Karpukhin
Vera Gor
Asli Celikyilmaz
ArXivPDFHTML
Abstract

Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don't generalize across all generation tasks. We show that soft-prompt based conditional text generation can be improved with simple and efficient methods that simulate modeling the discourse structure of human written text. We investigate two design choices: First, we apply \textit{hierarchical blocking} on the prefix parameters to simulate a higher-level discourse structure of human written text. Second, we apply \textit{attention sparsity} on the prefix parameters at different layers of the network and learn sparse transformations on the softmax-function. We show that structured design of prefix parameters yields more coherent, faithful and relevant generations than the baseline prefix-tuning on all generation tasks.

View on arXiv
Comments on this paper