Papers with Instruction-Aware Prompt Tuning

1 papers
IAPT: Instance-Aware Prompt Tuning for Large Language Models (2024.acl-long)

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Challenge: Existing methods for prompt tuning require many soft tokens to guarantee performance . large language models still require a large amount of GPU memory and computations to fine-tune .
Approach: They propose to use a parameter-efficient soft prompt generator to generate idiosyncratic soft prompts for each input instruction.
Outcome: The proposed method outperforms the baselines with comparable tunable parameters and is more efficient than LoRA under the single-backbone multi-tenant setting.

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