Papers with gradient-guided search

2 papers
Universal Adversarial Triggers for Attacking and Analyzing NLP (D19-1)

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Challenge: Using adversarial triggers, a model can produce a specific prediction . adversarial attacks are useful for evaluation and interpretation .
Approach: They propose a gradient-guided search over tokens that finds short adversarial triggers that successfully trigger the target prediction.
Outcome: The proposed algorithm finds short trigger sequences that successfully trigger the target prediction.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)

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Challenge: Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process.
Approach: They propose a method to create prompts for a diverse set of tasks using a gradient-guided search.
Outcome: The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning.

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