mlsanityData to 7 Oct 2026

Few-Shot Natural Language Inference Generation with PDD: Prompt and Dynamic Demonstration

arXiv 2205.10593 ↗First posted 21 May 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

Natural Language Inference Generation task is to generate a text hypothesis given a text premise and a logical relation between the two. This task can be used in data augmentation and controllable text generation in practice. In this paper, we propose language models with prompt and dynamic demonstration (LM-PDD) to tackle this problem in few-shot settings. Our framework outperforms standard fine-tuned models with low resource, achieving an average 8% absolute improvement on SNLI and MNLI datasets, and the results on 13 natural language classification tasks also show that our dynamic demonstration method has good generalizability.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 45 references that are in the map. First posted on arXiv 21 May 2022.

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References · 34

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22 more
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