– Pre-trained language models (PLMs) have revolutionized NLP tasks.
– PLMs can be vulnerable to backdoor attacks, compromising their behavior.
– Existing backdoor removal methods rely on trigger inversion and fine-tuning.
– PromptFix proposes a novel backdoor mitigation strategy using adversarial prompt tuning.
– PromptFix uses soft tokens to approximate and counteract the trigger.
– It eliminates the need for enumerating possible backdoor configurations.
– PromptFix preserves model performance and reduces backdoor attack success rate.
– Solo Performance Prompting (SPP) enhances problem-solving abilities in complex tasks.
– SPP reduces factual hallucination and maintains strong reasoning capabilities.
– Cognitive synergy emerges in GPT-4 but not in less capable models.
– Solo Performance Prompting (SPP) helps a computer program think like different people.
– It uses different personas to solve problems and get accurate knowledge.
– SPP reduces mistakes and makes better plans compared to other methods.
– It works well in tasks like writing stories and solving puzzles.
– SPP is better in GPT-4 model compared to other models.