PG-CE: A Progressive Generation Dataset with Constraint Enhancement for Controllable Text Generation

• URL: http://arxiv.org/abs/2509.17669v1
• Date: Mon, 22 Sep 2025 12:12:41 GMT
• Title: PG-CE: A Progressive Generation Dataset with Constraint Enhancement for Controllable Text Generation
• Authors: Yan Zhuang, Yuan Sun,
• Abstract summary: Controllable Text Generation (CTG) has become a critical technology for enhancing system reliability and user experience.<n>This paper proposes the PG-CE approach, which decomposes CTG tasks into three steps: type prediction, constraint construction, and guided generation.
• Score: 17.481794597546322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of Large Language Models (LLMs), Controllable Text Generation (CTG) has become a critical technology for enhancing system reliability and user experience. Addressing the limitations of traditional methods, this paper proposes the PG-CE (Progressive Generation with Constraint Enhancement) approach, which decomposes CTG tasks into three steps: type prediction, constraint construction, and guided generation. This method employs constraint generation models to dynamically build multi-dimensional constraints including tone, expression style, and thematic focus to guide output. Experiments demonstrate that PG-CE significantly improves generation quality across multiple scenarios while maintaining text controllability, thematic relevance, and response practicality. The research developed a dataset containing 90,000 constraint-text pairs (with an 8:2 ratio between daily and other topics), effectively reflecting real-world application requirements.

Related papers

• DraCo: Draft as CoT for Text-to-Image Preview and Rare Concept Generation [69.69738832428543]
  We propose Draft-as-CoT (DraCo), a novel interleaved reasoning paradigm for enhanced text-to-image generation.<n>Our method first generates a low-resolution draft image as preview, providing more concrete and structural visual planning and guidance.<n>DraCo achieves a tremendous increase on GenEval (+8%), Imagine-Bench (+0.91), and GenEval++ (+3%)
  arXiv  Detail & Related papers  (2025-12-04T18:59:53Z)
• AgentCTG: Harnessing Multi-Agent Collaboration for Fine-Grained Precise Control in Text Generation [10.208001921870709]
  Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation.<n>This paper introduces a novel and scalable framework, AgentCTG, which aims to enhance precise and complex control over the text generation.<n>AgentCTG achieves state-of-the-art results on multiple public datasets.
  arXiv  Detail & Related papers  (2025-09-17T04:07:22Z)
• Personalized Text Generation with Contrastive Activation Steering [63.60368120937822]
  We propose a training-free framework that disentangles and represents personalized writing style as a vector.<n>Our framework achieves a significant 8% relative improvement in personalized generation while reducing storage requirements by 1700 times over PEFT method.
  arXiv  Detail & Related papers  (2025-03-07T08:07:15Z)
• A Combined Encoder and Transformer Approach for Coherent and High-Quality Text Generation [5.930799903736776]
  This research introduces a novel text generation model that combines BERT's semantic interpretation strengths with GPT-4's generative capabilities. The model enhances semantic depth and maintains smooth, human-like text flow, overcoming limitations seen in prior models.
  arXiv  Detail & Related papers  (2024-11-19T01:41:56Z)
• Controllable Text Generation for Large Language Models: A Survey [27.110528099257156]
  This paper systematically reviews the latest advancements in Controllable Text Generation for Large Language Models. We categorize CTG tasks into two primary types: content control and control. We address key challenges in current research, including reduced fluency and practicality.
  arXiv  Detail & Related papers  (2024-08-22T17:59:04Z)
• Toward Unified Controllable Text Generation via Regular Expression Instruction [56.68753672187368]
  Our paper introduces Regular Expression Instruction (REI), which utilizes an instruction-based mechanism to fully exploit regular expressions' advantages to uniformly model diverse constraints. Our method only requires fine-tuning on medium-scale language models or few-shot, in-context learning on large language models, and requires no further adjustment when applied to various constraint combinations.
  arXiv  Detail & Related papers  (2023-09-19T09:05:14Z)
• Tractable Control for Autoregressive Language Generation [82.79160918147852]
  We propose to use tractable probabilistic models (TPMs) to impose lexical constraints in autoregressive text generation models. We show that GeLaTo achieves state-of-the-art performance on challenging benchmarks for constrained text generation. Our work opens up new avenues for controlling large language models and also motivates the development of more expressive TPMs.
  arXiv  Detail & Related papers  (2023-04-15T00:19:44Z)
• PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation [47.97523895218194]
  We propose a novel generation framework leveraging autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. Our framework enriches the Transformer decoder with latent representations to maintain sentence-level semantic plans grounded by bag-of-words.
  arXiv  Detail & Related papers  (2022-03-17T05:52:35Z)
• COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics [69.8062252611486]
  Cold decoding is a flexible framework that can be applied directly to off-the-shelf left-to-right language models. Our experiments on constrained generation tasks point to the effectiveness of our approach, both in terms of automatic and human evaluation.
  arXiv  Detail & Related papers  (2022-02-23T18:59:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.