Synthetic text generation is the process of using AI models or rules to create new, artificial text that mimics the style, structure, and patterns of real human writing. The output is original wording. It does not copy any one person’s answer.<\/p>\n\n\n\n
In survey research, synthetic text data usually means open-ended responses, which are answers respondents type in their own words instead of picking from a list. It can also cover comments, chat transcripts, and interview-style answers.<\/p>\n\n\n\n
Synthetic text is one branch of synthetic data<\/a>, the wider family that also includes artificial tables, images, and sensor readings. What sets text apart is that language is messy. Tone, slang, hesitation, and context all matter, so text is harder to fake well than a column of numbers.<\/p>\n\n\n\n One point deserves emphasis. A synthetic answer is not a respondent. Nobody lived the experience it describes, so it can show you how a comment might look, not what your customers actually feel.<\/p>\n\n\n\n\n\n Most modern synthetic text generation relies on large language models (LLMs), which are AI systems trained on huge collections of text to predict and produce fluent language. Older template and rule-based methods still work for simple jobs (these synthetic data generation techniques<\/a> cover them), but LLM synthetic data now covers most real projects. Three approaches span the range, and they differ mainly in how much real data they touch.<\/p>\n\n\n\n The simplest route is to write a prompt, which is a set of instructions telling the model what to produce. Here is a small example:<\/p>\n\n\n Prompt:<\/strong><\/span> Write 3 short comments from US shoppers who abandoned a checkout page. Vary the tone and length.<\/em><\/p>\n Output 1: Shipping cost showed up at the very end. Felt like a bait and switch.<\/p>\n Output 2: Had to create an account just to buy socks, so I left.<\/p>\n Output 3: Page kept timing out on my phone. Will try again later, maybe.<\/p>\n<\/blockquote>\n\n\n This approach needs no real data and takes minutes. The trade-off is fit. The model draws on general patterns from its training text, not on your audience, so the output tends to sound like an average voice. Notice how tidy those comments are. Real comments include typos, half sentences, and tangents.<\/p>\n\n\n\n A stronger route is grounding, which means feeding the model real examples or fine-tuning it. Fine-tuning is the practice of training an existing model further on your own text so it picks up your audience’s vocabulary and concerns.<\/p>\n\n\n\n Grounding matters. In a widely cited 2024 study, researchers built AI agents from interview transcripts of 1,052 people. According to the study on arXiv<\/a>, the agents replicated participants’ General Social Survey answers 85 percent as accurately as participants replicated their own answers two weeks later. The agents also showed smaller accuracy gaps across racial and ideological groups than agents given only demographic descriptions.<\/p>\n\n\n\n Keep the limits in view. Those tests used structured survey items, not free-text answers, so the result does not automatically carry over to open-ended writing.<\/p>\n\n\n\n If the source text is sensitive, you can add protection at training time. Differential privacy is a mathematical technique that limits how much any single person’s data can shape the model’s output.<\/p>\n\n\n\n One ACL 2023 paper<\/a> reports that fine-tuning a pretrained language model with differential privacy produced useful synthetic text with strong privacy protection. NIST adds a caution in its overview of differentially private synthetic data<\/a>. Many synthetic data techniques do not meet differential privacy at all, and accuracy remains the main challenge for those that do.<\/p>\n\n\n\n Synthetic text generation is the technique. The other terms below are outputs, users, or neighbors of that technique, and mixing them up leads to confused briefs and wrong expectations.<\/p>\n\n\n\nHow does synthetic text generation work?<\/strong><\/h2>\n\n\n\n
Prompting a general-purpose model<\/h3>\n\n\n\n
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Grounding the model in real data<\/h3>\n\n\n\n
Privacy-preserving synthetic text with differential privacy<\/h3>\n\n\n\n
How does synthetic text differ from synthetic survey responses and related terms?<\/strong><\/h2>\n\n\n\n