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Research map / Large language models / Language, safety and interpretability

Plug and Play Language Models: A Simple Approach to Controlled Text Generation

ICLR 2020 · Large language models › Language, safety and interpretability · approach: Controlled text generation · 138 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add gradient-based latent updates for topic control (ablation)GPT-2 topic generation · Topic control (7 topics)Topic relevance (%): 11.1 -> 46.9Helped
Rerank multiple samples without latent updates (ablation)GPT-2 topic generation · Topic control (7 topics)Topic relevance (%): 11.1 -> 15.8Helped
Add reranking to latent updates (ablation)PPLM topic generation · Topic control (7 topics)Topic relevance (%): 46.9 -> 51.7Helped
Add latent updates to unchanged GPT-2 sampling (ablation)GPT-2 sentiment generation · Sentiment controlSentiment accuracy (%) human: 19.3% -> 39.6%Helped
Rerank multiple samples without latent updates (ablation)GPT-2 sentiment generation · Sentiment controlSentiment accuracy (%) human: 19.3% -> 41.5%Helped
Add reranking to latent updates (ablation)PPLM sentiment generation · Sentiment controlSentiment accuracy (%) human: 39.6% -> 73.7%Helped
Use latent updates without reranking (ablation)GPT-2 sentiment generation · Sentiment controlFluency: 3.54±1.08 -> 2.79±1.17Hurt
Steer with the negative gradient of a toxicity classifierGPT-2 · Adversarial triggersToxicity (%): 63.6% -> 4.6%Helped

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