Control Prefixes for Parameter-Efficient Text Generation

15 Oct 2021  ·  Jordan Clive, Kris Cao, Marek Rei ·

Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application. However, it uses the same dataset-level tuned prompt for all examples in the dataset. We extend this idea and propose a dynamic method, Control Prefixes, which allows for the inclusion of conditional input-dependent information, combining the benefits of prompt tuning and controlled generation. The method incorporates attribute-level learnable representations into different layers of a pre-trained transformer, allowing for the generated text to be guided in a particular direction. We provide a systematic evaluation of the technique and apply it to five datasets from the GEM benchmark for natural language generation (NLG). Although the aim is to develop a parameter-efficient model, we show Control Prefixes can even outperform full fine-tuning methods. We present state-of-the-art results on several data-to-text datasets, including WebNLG.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Text Simplification ASSET Control Prefixes (BART) SARI (EASSE>=0.2.1) 43.58 # 3
FKGL 5.97 # 1
QuestEval (Reference-less, BERTScore) 0.64 # 1
Data-to-Text Generation Cleaned E2E NLG Challenge Control Prefixes (T5-large) BLEU (Test set) 44.15 # 1
Text Generation DART Control Prefixes (T5-large) METEOR 0.411 # 1
Text Simplification TurkCorpus Control Prefixes (BART) SARI (EASSE>=0.2.1) 42.32 # 3
FKGL 7.74 # 2
QuestEval (Reference-less, BERTScore) 0.66 # 1
Data-to-Text Generation WebNLG Control Prefixes (A1, T5-large) BLEU 67.32 # 1
Data-to-Text Generation WebNLG Control Prefixes (A1, A2, T5-large) BLEU 67.15 # 2
Data-to-Text Generation WebNLG Full Control Prefixes (A1, T5-large) BLEU 61.94 # 2
Data-to-Text Generation WebNLG Full Control Prefixes (A1, A2, T5-large) BLEU 62.27 # 1


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