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Exploring Controllable Text Generation Techniques

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arxiv 2005.01822 v2 pith:RA2OAVSV submitted 2020-05-04 cs.CL

Shrimai Prabhumoye , Alan W Black , Ruslan Salakhutdinov This is my paper · ORCID
classification cs.CL
keywords generationmodulescontrollabletechniquestextprocesstherework
verification ladder T0 review T1 audit T2 compute T3 formal
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Neural controllable text generation is an important area gaining attention due to its plethora of applications. Although there is a large body of prior work in controllable text generation, there is no unifying theme. In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules. The control of attributes in the generation process requires modification of these modules. We present an overview of different techniques used to perform the modulation of these modules. We also provide an analysis on the advantages and disadvantages of these techniques. We further pave ways to develop new architectures based on the combination of the modules described in this paper.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MapExplorer: New Content Generation from Low-Dimensional Visualizations

    cs.AI 2024-12 conditional novelty 6.0 of 10

    A new task and benchmark that generate contextually aligned text for arbitrary coordinates in 2D projection maps, evaluated with an LLM-based metric.

  2. ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks

    cs.CL 2024-12 conditional novelty 6.0 of 10

    FormatBench evaluates LLM format adherence across ten tasks, and REFF uses format-checker rewards in PPO to raise format faithfulness substantially while keeping content quality roughly stable.

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