Prompt Guided Diffusion for Controllable Text Generation (2025)

Abstract: Controlled text generation, originally a task to generate coherent, contextually relevant text with specified attributes such as sentiment, topic, or style, has seen a lot of development with methods that use PPLM, FUDGE, and diffusion-based models. However, most state-of-the-art methods balance control precision with fluency. Classifier-guided approaches, like PPLM, are well-known for unstable updates of gradients, yielding incoherent outputs, while autoregressive models, like FUDGE, depend on rigid templates that limit creativity. While recent diffusion models show promises in iterative refinement and diversity, they often lack mechanisms to explicitly incorporate task-specific knowledge and hence require various complicated auxiliary classifiers for training and inference.We now propose a prompt-guided diffusion framework that integrates structured prompts seamlessly into the process of diffusion for precise and flexible control of generated texts.Each prompt combines a target condition (e.g., sentiment label), an in-class example (e.g., a positive movie review), and a placeholder for the generated sentence. Explicit, human-readable guidance is thereby given, spanning high-level intent to low-level text generation.Our approach encodes prompts using large pre-trained language models, e.g., BART, fusing these in a cross-attention manner with the diffusion dynamics, achieves new state-of-the-art results for all benchmarks, including IMDB for sentiment, AG News for topic, and E2E for structured data-to-text generation.

## introduction text generation: a computational paradigm for producing meaningful written content with coherence, often fueled by nlp models. its uses include chatbots, content generation systems, machine translation, and other areas. controllability in text generation concerns the ability to control the outputs for desired characteristics -including tone, style, length, or topic based on predefined criteria or user preference. this is usually done through all sorts of means, from prompt engineering to fine-tuning or control tokens. unconstrained generation, on the other hand, refers to cases where the generated content deviates from the requirements, resulting in out-of-topic content. such deviations (known informally as use performance error) are common due to the inherently random nature of sampling or subtle modeling of user intention, additional work is often needed in production to find a satisfactory balance between controllability and creativity in the model. nlp tasks related to text generation generally relate to a generation task where models attempt to create a set of coherent, meaningful strings from some input, based on generative architectures. researchers have developed various types of generative strategies. generative adversarial networks (gans) compete against a discriminator to generate text samples. ebms work by defining an energy function across the text data, with the model trained to produce lower energy for valid samples and higher energy for invalid samples. this allows for a flexible way to enforce constraints during the generation process. flow-based models produce exact likelihoods by invertible mapping from simple probability distributions to complex ones, giving much more control. diffusion models progressively synthesize outputs, denoising random noise through multiple probabilistic steps, yielding stable and high-quality results. these paradigms together illustrate the spectrum of mechanisms for text generation by arranging different trade-offs between controllability, diversity, and fidelity. this paper focuses on the application of diffusion models to the task of text generation. ## the proposed method prompt diffusion is an emerging key mechanism for generative modeling, providing a simple yet powerful way to condition outputs of a diffusion model with standard language prompts. while diffusion models have been shown to be powerful samplers (from images to audio to text), achieving explicit, fine-grained control has remained a challenge @xcite . this is what makes direct control over the generative process and steering it toward certain outputs complex. to address this problem, diffusion strategies based on prompts condition the diffusion model on text descriptions (i.e., "prompts") that describe the desired properties and guide the generative process of the model. essentially, a big pre-training language model, e.g. bart, is applied to these textual prompts to turn them into vector representations that contain the prompt's semantic content. these representations are then fed into the denoising network, often using concatenation methods, guiding concerning the prompt during each step of the denoising process. one particular type of structured prompt uses the target property, such as a sentiment or topic, along with a randomly selected in-class example (to prevent data overlap or leakage), then leaves a blank for the new sentence. our diffusion model, which is based on a transformer, manages the noisy text embeddings with cross-attention conditioning on the embeddings of prompt processed by bart (or any other similar encoder). this design is shown in figure 1 . several advantages come with prompt-based diffusion. first, it is highly controllable @xcite : with meticulously engineered prompts, one can dictate the style, content, or other types of attributes, allowing for highly constrained creative output @xcite . second, it offers versatility: a single pre-trained diffusion model can be used on many tasks by simply changing a prompt instead of fine-tuning each model for each new objective. it is extremely cost-efficient. third, it offers great potential for few-shot or in-context learning, allowing the model to infer instructions from a few examples @xcite . but writing good prompts is not trivial: badly written prompts give bad results, and the encoding of prompts also takes time to generate. complex prompts may raise challenges as well in terms of coherence. despite that, prompt-based diffusion is an appealing method, as it provides extensive user-driven guidance combined with the powerful generative capability of extensive diffusion models. our system combines a large language model and diffusion for conditional text generation. we adopt a prompt-learning paradigm that concatenates the condition label (e.g., sentiment) with a relevant example review to form a textual prompt. subsequently, this prompt is encoded (e.g., with bart), bringing about embeddings that guide the diffusion process. the diffusion model is trained to predict the noise added at each time step, effectively modeling the reverse diffusion. therefore, at inference time, random noise is iteratively converted into meaningful embeddings based on the guidance of the prompt. so those final embeddings get passed through a bart decoder, benefiting from the pre-trained autoregressive decoding to produce reasonable text. this pipeline elegantly resolves the shortcomings of a completely embedding-based decoding (which can be compelled to revert to rough nearestneighbor lookups) and produces high-quality text outputs. the prompt method informs the output using the desired condition in the prompt but also dictates the context with the example text, which provides a granular steer for what you would want to get as output. by leveraging the strengths of a sizable, pre-trained encoder-decoder (bart) and a purpose-built diffusion model, the components are tailored for their tasks, yielding robust performance on conditional text generation tasks. ## experimantal results we evaluate our approach on three benchmark datasets: imdb (50,000 movie reviews for sentiment analysis) @xcite , ag news (120,000 news articles across four topics: world, sports, business, and sci/tech) @xcite , and e2e (a data-to-text dataset which involves restaurant descriptions using structured attribute-value pairs) @xcite . each dataset poses different challenges: imdb for sentiment polarity, ag news for topic coherence, and e2e for structured semantic fidelity. our diffusion training involves two steps: a forward phase where the text embeddings are gradually contaminated with gaussian noise across @xmath0, and a reverse phase, during which a trained model denoises the signal to reconstruct the data. in the forward step, x 0 progressively transforms into x t according to a noise schedule β @xmath1.9, ending with a nearly random noise state (equation 1 ). the model is trained to predict the noise at each time t concerning the variational bound (equation 4 ), estimating p θ (x t-1 | x t ). diffusion-based generators instantiate text through a multi-step, iterative denoising process, allowing fine-grained modifications during intermediate steps to satisfy conditions such as syntactic or stylistic properties instead of generating the text token-by-token as in autoregressive models. this iterative routine stabilizes training and provides more robust controllability than singlepass methods. our experiments were conducted using the e2e dataset, and the results showed that the proposed method outperformed other approaches. for the semantic content task, we supplied a field (e.g., rating) and provided a value (e.g., 5 stars) to compute a sentence that would accurately personify the relationship between the field and value provided, and the ground truth for the same being the exact match of the 'value'. for the parts-ofspeech task, we generated a sequence of pos tags to the model (e.g., pronoun verb determiner noun) and asked it to output a word sequence of the same length such that the pos tags were aligned with the target according to an oracle pos tagger. success was measured using word-level exact matches. for the length task, we defined a desired length between 10 and 40 and produced a sequence of up @xmath2. we also conduct experiments on imdb and ag-news, assessing their quality using metrics such as bleu, rough, and bertscore as shown in the numerical results in diffusion models give controllable text generation more flexibility and come with significantly more advantages than autoregressive, vae, or gan-based approaches. while autoregressive models like gpt predict following a static, tokenby-token order, diffusion models slowly guide latent representations through many iterations. such progressive denoising lends itself well to making subtle tweaks to fit our constraints, such as syntax, length, or style. diffusion models manage to strike the right balance between accuracy and creativity in comparison to classifier-guided techniques like pplm, which tend to generate unintelligible outputs owing to erratic updates of gradients, or vaes that in most cases hit a wall when it comes to diversity. diffusion models achieve a balance for generation by inserting structured prompts (e.g. target attributes) into continuous input via cross-attention mechanisms without losing fluency. ## conclusion in this work, we propose a prompt-guided diffusion framework for controllable text generation that mitigates critical limitations of the existing methods in balancing precision and fluency. our method integrates structured prompts that combine target conditions and in-class examples into the diffusion process, achieving fine-grained control over attributes such as sentiment, topic, and adherence to structured data. dynamic sampling of examples during training ensures robustness to intra-class diversity. future work might investigate hybrid models that combine the proposed prompt-guided diffusion either with the retrieval-augmented generation or few-shot learning, as well as an extension to multimodal tasks. such a framework advances the frontier of controllable text generation by bridging human intention with generative ai through intuitive prompting and thus offers a flexible and scalable solution for real-world deployment.