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We explore incorporating natural language inference (NLI) into the textgenerative pipeline by using a pre-trained NLI model to assess whether agenerated sentence entails, contradicts, or is neutral to the prompt andpreceding text. First, we show that the NLI task is predictive of generationerrors made by GPT-3. We use these results to develop an NLI-informedgeneration procedure for GPT-J. Then, we evaluate these generations byobtaining human annotations on error types and overall quality. We find that anNLI strategy of maximizing entailment improves text generation when the nucleussampling randomness parameter value is high, while one which maximizescontradiction is in fact productive when the parameter value is low. Overall,though, we demonstrate that an NLI strategy of maximizing the neutral classprovides the highest quality of generated text (significantly better than thevanilla generations), regardless of parameter value.

For Generated Text, Is NLI-Neutral Text the Best Text?