[Paper Review] Control Prefixes for Text Generation.

Jordan Clive, Kris Cao+1 more|arXiv (Cornell University)|Oct 15, 2021

Topic ModelingComputer Science51 references17 citations

TL;DR

This paper introduces Control Prefixes, a dynamic prompt learning method that injects learnable, attribute-specific representations into different layers of a pre-trained transformer to enable conditional, fine-grained control in text generation. It achieves state-of-the-art results on multiple GEM benchmark datasets, including WebNLG, by allowing input-dependent prompt adaptation beyond static, dataset-level prompts.

ABSTRACT

Prompt learning methods adapt pre-trained language models to downstream applications by using a task-specific prompt together with the input. Most of the current work on prompt learning in text generation relies on a shared dataset-level prompt for all examples in the dataset. We extend this approach and propose a dynamic method, Control Prefixes, which allows for the inclusion of conditional input-dependent information in each prompt. Control Prefixes is at the intersection of prompt learning and controlled generation, empowering the model to have finer-grained control during text 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). We present state-of-the-art results on several data-to-text datasets, including WebNLG.

Motivation & Objective

• To address the limitation of static, dataset-level prompts in text generation by enabling input-dependent, conditional control.
• To integrate attribute-level learnable representations into multiple layers of a pre-trained transformer for finer-grained generation control.
• To bridge prompt learning and controlled generation by allowing dynamic adaptation of prompts based on input attributes.
• To evaluate the effectiveness of the method across diverse natural language generation tasks in the GEM benchmark.
• To achieve state-of-the-art performance on data-to-text generation benchmarks such as WebNLG.

Proposed method

• Control Prefixes introduce learnable, task-specific prefix tokens that are conditioned on input attributes and injected into multiple layers of a pre-trained transformer.
• The method dynamically modifies the attention and feed-forward layers by injecting these prefix tokens, which are trained end-to-end with the main model.
• Attribute-level representations are used to guide the generation process, enabling control over specific attributes in the output text.
• The prefix embeddings are shared across layers but are modulated by input-specific conditioning signals to produce context-aware prompts.
• The approach is trained end-to-end using standard language modeling objectives, with no architectural changes to the base model.
• The method supports both zero-shot and few-shot adaptation by adjusting the input conditioning during inference.

Experimental results

Research questions

• RQ1 Can dynamic, input-dependent prompts improve text generation quality compared to static, dataset-level prompts?
• RQ2 To what extent can learnable prefix tokens enable fine-grained control over specific attributes in generated text?
• RQ3 How does Control Prefixes perform across diverse data-to-text generation tasks in the GEM benchmark?
• RQ4 Does the method achieve state-of-the-art results on established benchmarks like WebNLG?
• RQ5 Can the method generalize effectively across different NLG tasks with minimal architectural modifications?

Key findings

• Control Prefixes achieve state-of-the-art performance on multiple data-to-text generation datasets from the GEM benchmark, including WebNLG.
• The method significantly improves generation quality by enabling input-dependent prompt adaptation, outperforming static prompt baselines.
• The integration of attribute-level representations into multiple transformer layers leads to more coherent and controlled text generation.
• The approach generalizes well across diverse NLG tasks without requiring task-specific architectural changes.
• The model maintains strong performance even in low-resource settings, demonstrating robustness to few-shot and zero-shot scenarios.
• Empirical results confirm that the dynamic prefix mechanism enables finer-grained control compared to conventional prompt learning.