Can LLMs Replace Survey Respondents?
Recent research explores the potential of large language models (LLMs) to simulate household survey responses regarding inflation. While LLMs can replicate average responses closely, they struggle with representing the diversity of opinions found in real surveys. Techniques like unlearning are being investigated to improve the accuracy of LLM-generated responses by addressing issues like mode collapse.
- ▪LLMs can replicate average survey responses closely, achieving results within a percentage point of actual data.
- ▪However, LLMs tend to produce responses that lack the diversity of real-world opinions, often clustering around a narrow range.
- ▪Unlearning methods are being tested to enhance the dispersion of LLM responses and better reflect varied public opinions.
Towards Data Science files mainly under ai. We currently carry 100 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/can-llms-replace-survey-respondents/ |
| Publication time | Wed, 20 May 2026 18:26:36 +0000 |
| Retrieval time | 2026-05-20T18:40:02.940Z |
| Last seen | 2026-05-20T18:40:02.940Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | aCjXzvvagz_3 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Large Language Models Can LLMs Replace Survey Respondents? How unlearning fixes mode collapse in synthetic survey replies Moritz Pfeifer May 20, 2026 9 min read Share What happens when you ask an LLM to simulate 6,000 American households answering questions about inflation? Recent papers find that large language models can replicate the average responses of major household surveys to within a percentage point (Zarifhonarvar, 2026). In 2020, the Survey of Consumer Expectations (SCE) reported a one-year-ahead median inflation rate of about 3%. The median produced by a prompted LLM with realistic personas and a knowledge-cutoff instruction: also about 3%.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
Discussion
0 commentsMore from Towards Data Science
