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arXiv · 2205.11055

TempLM: Distilling Language Models into Template-Based Generators

Tianyi Zhang·Mina Lee·Lisa Li·Ende Shen·Tatsunori B. Hashimoto
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Abstract

While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic template-based systems provide strong guarantees of faithfulness at the cost of fluency. We propose TempLM, which achieves the best of both worlds by distilling a PLM into a template-based generator. On the E2E and SynthBio data-to-text datasets, we show that TempLM is more faithful than the original PLM and is more fluent than prior template systems. Notably, on an out-of-domain evaluation, TempLM reduces a finetuned BART model's unfaithfulness rate from 83% to 0%. In a human study, we find that TempLM's templates substantially improve upon human-written ones in BERTScore.

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Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen, Tatsunori B. Hashimoto. 2022-05-23. TempLM: Distilling Language Models into Template-Based Generators. https://arxiv.org/abs/2205.11055

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