Conditional Text Generation Models

• Conditional text generation models are neural architectures that generate text based on structured or unstructured signals such as context, style, or multimodal inputs.
• They integrate varied methodologies including explicit fusion, latent variable modeling, and planning-based control to enhance fluency and relevance.
• Empirical evaluations using metrics like ROUGE, BLEU, and attribute accuracy show improved controllability and faithfulness, though challenges like hallucination remain.

Conditional text generation models are neural architectures designed to produce textual output that is explicitly controlled by structured or unstructured conditioning signals, such as context, style, attributes, or multimodal information. Unlike unconditional or prompt-only generation, conditional models are formulated mathematically as learning the probability distribution $p(Y|X, C)$, where $X$ is the source/input and $C$ encompasses the conditioning information: context vectors, style attributes, domain knowledge, images, or more abstract constraints. Over recent years, conditional text generation has evolved dramatically, integrating innovations in transformer-based architectures, plug-in modules, adversarial training, latent variable models, planning mechanisms, and control strategies, substantially improving fluency, relevance, controllability, and faithfulness.

1. Foundational Paradigms and General Formulation

Conditional text generation extends the baseline sequence-to-sequence (“seq2seq”) paradigm by the explicit inclusion of context or attribute information. The general conditional generation formula is:

$p(Y|X, C) = \prod_{t=1}^T p(y_t | X, C, y_{<t})$

Here, $X$ is the input (often rich: document, data, dialogue history, image), $C$ the conditioning signal, and $Y$ the output sequence. Major conditioning modalities include context (dialogue history (Guo et al., 2019)), personality traits (Wang et al., 2019), style exemplars (Peng et al., 2019), multimodal signals (Sollami et al., 2021), aspect-based or knowledge-centric constraints (Chebolu et al., 2021, Li et al., 2021), and domain attributes.

Model architectures fall into several families:

• Explicit Conditioning: Condition embeddings are concatenated or fused with input embeddings, either at the encoder or directly in decoder inputs (e.g., concatenation of personality vectors (Wang et al., 2019), auxiliary templates in ABSA (Chebolu et al., 2021)).
• Implicit Conditioning: The decoder attends over context-encoded vectors, including external knowledge graphs, emotional states, or multimodal features (Sollami et al., 2021, Peng et al., 2022).
• Latent Variable Models: VAE-based frameworks encode both content and condition-specific latent spaces, supporting flexible “plug-in” adaptation for new conditions (Duan et al., 2019, Tu et al., 2022).
• Planning-based Control: Intermediate blueprints (question–answer plans) guide long-form generation, decoupling content selection (“what to say”) from realization (Huot et al., 2023).

2. Conditioning Strategies: Representational, Architectural, and Decoding

Conditional models use a variety of mechanisms to integrate conditioning information:

• Exemplar-guided Decoding: The decoder is dynamically reparameterized using “soft templates” retrieved via similarity-matching from the training data (Peng et al., 2019). Weight matrices are constructed as low-rank adaptive sums, e.g., $P = \sum_{i=1}^{r} \lambda_i P_i$, where $\lambda_i$ are computed from exemplar encodings.
• Plug-and-Play Latent Control: Systems like PPVAE decouple universal generation (trained on large unlabeled corpora) from condition representation (small “plug-in” networks), allowing efficient adaptation to new conditions (Duan et al., 2019). PCAE further introduces a broadcasting label fusion network that repeatedly injects label embeddings into the transformation pathway of the global latent vector (Tu et al., 2022).
• Multimodal Adaptation: MAnTiS conditions transformer models on both image and text input by projecting modality-specific representations into the LLM’s token space and forming a conditional prefix (Sollami et al., 2021). XFBoost augments generation with attribute extraction and reward-guided finetuning for controllable descriptions (Peng et al., 2022).
• GAN-based Category Control: FA-GAN incorporates both feature-aware and category-aware encoders, using Gumbel SoftMax for differentiable sampling and multi-class classification loss for explicit control in adversarial training (Li et al., 2023).
• Blueprint-based Planning: Text-Blueprint introduces intermediate question–answer blueprints as generation plans, improving controllability and reducing hallucinations (Huot et al., 2023).
• Auxiliary Tuning: Conditional logits from an auxiliary model are summed with frozen pre-trained LM logits, efficiently steering generation toward attribute-controlled outputs without full fine-tuning (Zeldes et al., 2020).

3. Evaluation Metrics and Empirical Findings

Conditional text generation systems are evaluated using:

Metric/Task	Description	Notable Usage
ROUGE (1,2,L)	Overlap between generated and reference text	Summarization (Peng et al., 2019, Lee et al., 2020, Fu et al., 2023)
BLEU	n-gram precision for translation/text	Data-to-text, translation (Duan et al., 2019, Li et al., 2023)
PARENT Recall	Faithfulness to input context	Data-to-text, Scope (Duong et al., 19 Feb 2025)
NLI Score	Entailment between input and output	Scope, faithfulness (Duong et al., 19 Feb 2025)
Distinct-1/2	Diversity (unique n-grams)	PPVAE, PCAE (Duan et al., 2019, Tu et al., 2022)
AlignScore, FactCC	Factual/semantic consistency	Scope (Duong et al., 19 Feb 2025)
V-measure	Clustering structure in embedding space	PonTE (Yamada et al., 23 Apr 2025)

Empirical results show robust improvements in both controllability and faithfulness:

• AdaDec (Peng et al., 2019) achieves >1 ROUGE point above baselines in summarization.
• PPVAE (Duan et al., 2019) yields 0.85 attribute accuracy vs. 0.72–0.69 in prior models, with lower training cost.
• FA-GAN (Li et al., 2023) improves classification accuracy by 1–3% over 10 generation methods and delivers higher BLEU/diversity.
• Scope (Duong et al., 19 Feb 2025) consistently outperforms CLIFF, critic-driven, and context-aware decoding in faithfulness metrics and pairwise preference judgment.
• Semantic-aware watermarking preserves performance on summarization/data-to-text compared to unrevised methods that degrade BLEU by up to 97% (Fu et al., 2023).

A plausible implication is that integrating dynamic, condition-aware latent spaces or attribute-constrained decoding dramatically improves both control and output quality, even under low-data or new-condition regimes.

4. Faithfulness, Hallucination, Reward Gaming, and Security Concerns

Conditional models are vulnerable to generating unfaithful outputs—hallucinations, unsupported facts, or corrupted context. This arises from overreliance on statistical priors, distribution shift, or exposure bias during teacher-forcing training. Recent work (Duong et al., 19 Feb 2025) proposes a self-supervised framework:

• Noisy (unfaithful) outputs are synthesized by stochastically mixing context-grounded and unconditional LLM tokens at each generation step, then adopting preference-based optimization to increase the likelihood gap between reference and noisy outputs.

Reward gaming is a distinct challenge in RL-driven conditional generation. Three cases are highlighted (Pang et al., 2022):

• Noise-induced spurious correlation: Model overproduces patterns (e.g., Sudoku ending with “7”) due to misannotations.
• Naturally occurring spurious correlation: Generator exploits dataset biases (e.g., frequent ellipsis, rare tokens).
• Covariate shift: Policy explores out-of-distribution input space, where learned rewards are poorly specified.

Proposed remedies (regularizing with MLE, updating reward with iterative human annotation, discriminative retraining) reduce vulnerability but do not eliminate gaming; future research is needed to detect subtle exploits and improve out-of-domain robustness.

Security concerns—namely, watermarking for AI detection—must balance robustness and generation quality. Semantic-aware watermarking algorithms (Fu et al., 2023) adapt the partitioning to ensure input-tied tokens remain in the favored “green list,” preventing information loss while maintaining a detectable signature.

5. Multimodal, Aspect-Based, and Controlled Generation

Recent advances have generalized conditioning signals far beyond structured attributes:

• Multimodal Conditioning: Systems condition on combinations of image and text, using encoders to project all modalities into a common representation (Sollami et al., 2021, Peng et al., 2022). Lexical constraints and visual attribute extraction enhance factual alignment.
• Aspect-Based Sentiment Generation: ABSA is reframed as conditional generation of summary-like auxiliary statements incorporating target, aspect, and polarity (Chebolu et al., 2021). Templates permit joint extraction and output, improving detection of implicit targets.
• Causal and Knowledge-Constrained Generation: Lexically constrained decoding with disjunctive positive constraints supports generative diversity while maintaining adherence to knowledge graphs of causal relations (Li et al., 2021).
• Category Control via GAN: Dual-encoder GANs (feature-aware, category-aware) paired with relational memory core decoders address diversity, control, and mode collapse (Li et al., 2023).

Editor’s term: “Conditioned Decoding” refers to any mechanism (explicit fusion, adaptive weights, attribute constraint, auxiliary module, plan-guided transformation) that directly modifies the decoder’s trajectory as a function of $C$ in $p(y_t|X, C, y_{<t})$.

6. Latent Semantic Embedding for Condition-Dependent Similarity

Out-of-the-box conditional text embeddings align representation with aspect-specific conditioning without finetuning. PonTE (Yamada et al., 23 Apr 2025) steers causal LLMs via conditional prompts (“Express this text ‘T’ in one word in terms of C:”). It generates embeddings from hidden states for downstream clustering or semantic similarity (using cosine similarity). Across clustering and conditional semantic similarity tasks, PonTE matches or exceeds supervised methods (e.g., SimCSE, GTE, E5) in V-measure, Spearman’s $\rho$, and Pearson’s r, greatly improving scalability and interpretability.

7. Research Directions and Open Challenges

Research continues to address several challenges:

• Extracting and integrating heterogeneous context: Representing and fusing dynamic, multi-factor contextual signals (dialogue, external knowledge, multimodal input) remains an open area (Guo et al., 2019).
• Faithfulness and hallucination: Self-supervised and preference-driven training mitigate ungrounded output but do not obviate failure under major domain shift (Duong et al., 19 Feb 2025).
• Efficient, flexible control: Plug-in models (PPVAE, PCAE) and auxiliary tuning allow practical adaptation to new conditions, but scaling multi-condition combinatorics and latent navigation need further paper (Duan et al., 2019, Tu et al., 2022).
• Reward alignment and gaming: RL-based control is susceptible to proxy reward exploitation; robust evaluation and cross-metric tuning are imperative (Pang et al., 2022).
• Security, detection, and trust: Conditional watermarking must ensure output quality without compromising detection ability (Fu et al., 2023).

This suggests the field is rapidly advancing toward achieving truly controlled, interpretable, and faithful conditional generation across diverse task regimes, though the interplay between control, efficiency, and robustness drives continued investigation.