Controllable data-to-text is the NLP task of generating natural language from structured data with explicit control over output attributes — allowing users to guide the generation process by specifying desired style, content focus, length, formality, sentiment, or other properties while ensuring the text remains faithful to the input data.

What Is Controllable Data-to-Text?

• Definition: Data-to-text generation with user-specified control attributes.
• Input: Structured data + control signals (style, focus, length, etc.).
• Output: Text that describes the data AND follows control specifications.
• Goal: Generate text that is both faithful to data and matches desired attributes.

Why Controllability?

• Audience Adaptation: Technical vs. lay audience, expert vs. novice.
• Length Control: Brief summary vs. detailed description.
• Style Matching: Formal report vs. casual blog vs. conversational.
• Content Focus: Highlight specific aspects of the data.
• Personalization: Tailor output to individual user preferences.
• Editorial Control: Maintain brand voice and communication standards.

Control Dimensions

Content Control:

• What to say: Which data fields to describe.
• Emphasis: Which aspects to highlight or prioritize.
• Detail Level: How much detail for each field.
• Ordering: Sequence of information presentation.

Style Control:

• Formality: Formal/informal/casual register.
• Tone: Positive/neutral/critical/enthusiastic.
• Complexity: Reading level (Flesch-Kincaid grade).
• Voice: Active/passive, first/second/third person.

Length Control:

• Token Count: Exact or approximate target length.
• Sentence Count: Number of sentences to generate.
• Granularity: Single sentence vs. paragraph vs. multi-paragraph.

Domain Control:

• Vocabulary: Domain-specific terminology.
• Format: Report, email, caption, bullet points.
• Genre: News article, product review, academic paper.

Control Mechanisms

Prompt-Based Control:

• Include control instructions in LLM prompts.
• Example: "Write a formal, 3-sentence summary focusing on revenue."
• Benefit: Flexible, no architectural changes needed.
• Challenge: Control may be imprecise or ignored.

Control Tokens:

• Prepend special tokens encoding desired attributes.
• Example: + data input.
• Benefit: Direct, learned control signals.
• Implementation: CTRL, FLAN-style instruction tokens.

Conditional Training:

• Train model conditioned on control attributes + data.
• Model learns to generate differently based on conditions.
• Benefit: Fine-grained, reliable control.

Latent Space Manipulation:

• Manipulate hidden representations to control output.
• VAE-based approaches with controllable latent factors.
• Benefit: Smooth interpolation between control settings.

Post-Processing:

• Generate multiple candidates, filter by control criteria.
• Rerank based on alignment with control specifications.
• Benefit: Works with any generation model.

Evaluation

Faithfulness:

• Does the text accurately reflect the input data?
• Metrics: PARENT, entailment-based scores.

Controllability:

• Does the text match the specified control attributes?
• Metrics: Classifiers for style/tone, length matching, content coverage.

Quality:

• Is the text fluent and natural?
• Metrics: BLEU, BERTScore, perplexity, human fluency ratings.

Trade-offs:

• Control precision vs. fluency (more control can reduce naturalness).
• Often measured as Pareto frontier of controllability vs. quality.

Applications

• Personalized Reports: Different detail levels for different stakeholders.
• Multi-Audience Content: Same data, different presentations.
• Brand Voice: Consistent company voice across generated content.
• Accessibility: Simplified language for broader audiences.
• Multi-Lingual: Control target language alongside other attributes.

Key Research & Models

• CTRL (Salesforce): Control codes for conditional generation.
• PPLM: Plug and play language models for attribute control.
• GeDi: Generative discriminator guided generation.
• FUDGE: Future discriminators for generation control.
• InstructGPT/RLHF: Instruction following as a form of control.

Tools & Frameworks

• Models: GPT-4, Claude, Llama with instruction prompting.
• Libraries: Hugging Face Transformers, vLLM for inference.
• Control Libraries: PPLM, GeDi implementations.
• Evaluation: Custom classifiers for control attribute measurement.

Controllable data-to-text is the key to practical data narration — it enables generating text that not only faithfully represents data but matches the specific communication needs of each audience, context, and use case, making data-to-text applicable across diverse real-world scenarios.