Papers with learning costs

2 papers
Building a Personalized Dialogue System with Prompt-Tuning (2022.naacl-srw)

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Challenge: We build a dialogue system that can respond based on a given character setting (persona) this method is not suitable because the more persona information is added, the longer the input text becomes.
Approach: They propose to use prompt-tuning to build a dialogue system that responds based on a persona . they conduct automatic and manual evaluations on English and Japanese .
Outcome: The proposed method can build a dialogue system with more natural responses with less computational resources than fine-tuning.
FreeCtrl: Constructing Control Centers with Feedforward Layers for Learning-Free Controllable Text Generation (2024.acl-long)

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Challenge: Controllable text generation (CTG) focuses on crafting texts adhering to specific attributes . studies show learning-based methods require extensive computational and data resources .
Approach: They propose a learning-free approach that dynamically adjusts the weights of selected feedforward neural network vectors to steer the outputs of large language models.
Outcome: The proposed approach outperforms learning-based and learning-free methods on multi-attribute control.

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