---
title: "PLACES — paper by Chen et al. (2023) — summary and ideas"
description: "The authors also generated three-party conversations, which they describe as the first synthesis of… 61 references, cited by 61 papers here."
authors: ["Maximillian Chen", "Alexandros Papangelis", "Chenyang Tao", "Seokhwan Kim", "Andy Rosenbaum", "Yang Liu", "Zhou Yu", "Dilek Hakkani-Tur"]
year: 2023
venue: "Findings of the Association for Computational Linguistics: EACL 2023"
source: "https://aclanthology.org/2023.findings-eacl.63/"
doi: "10.18653/v1/2023.findings-eacl.63"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2023-places-prompting-language-models-social/"
---
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# PLACES: Prompting Language Models for Social Conversation Synthesis

**Authors:** Maximillian Chen, [Alexandros Papangelis](https://md.research.elamur.ai/free/en/authors/alexandros-papangelis/), Chenyang Tao, [Seokhwan Kim](https://md.research.elamur.ai/free/en/authors/seokhwan-kim/), [Andy Rosenbaum](https://md.research.elamur.ai/free/en/authors/andy-rosenbaum/), [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/), [Zhou Yu](https://md.research.elamur.ai/free/en/authors/zhou-yu/) and [Dilek Hakkani-Tur](https://md.research.elamur.ai/free/en/authors/dilek-hakkani-tur/) · **Year:** 2023 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2023](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2023](https://md.research.elamur.ai/free/en/venues/findings/2023/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.findings-eacl.63/) · [PDF](https://aclanthology.org/2023.findings-eacl.63.pdf) · [DOI 10.18653/v1/2023.findings-eacl.63](https://doi.org/10.18653/v1/2023.findings-eacl.63) · [OpenAlex](https://openalex.org/W4386566609)

**Citations:** 29 citations (OpenAlex, 2026-10-14) · [61](https://md.research.elamur.ai/free/en/papers/chen-2023-places-prompting-language-models-social/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Gathering good dialogue data through crowdsourcing is costly, and for some applications privacy or ethical constraints make it impossible. PLACES addresses this by building a whole social conversation dataset from a handful of expert-written examples. The authors wrote ten short two-speaker conversations, each paired with a "recipe" giving a topic and background facts about the speakers. These examples served as few-shot in-context demonstrations for open-source language models, mainly OPT-30B. The models then produced topic-conditioned conversations, with topics taken from the FITS topic combinations. Two kinds of evaluation followed. Human raters and automatic metrics judged the synthetic dialogues directly against DailyDialog and Topical Chat, and chatbots fine-tuned on each dataset were compared in interactive human evaluation. The synthetic data matched the human-collected corpora on both counts. The authors also generated three-party conversations, which they describe as the first synthesis of multi-party dialogue. Raters preferred these on every measured dimension over excerpts from a human-collected multi-party dataset. The work suggests that prompting can create training data for dialogue tasks that have no existing corpus.

## Key points
- The seed pool is only ten handwritten dyadic conversations with recipes (topic plus speaker background), which took the first author about 45 minutes to write.
- Each prompt contains three randomly sampled seed conversations. Generation uses OPT-30B with nucleus sampling (p = 0.92), after the authors also tried GPT-J 6B and GPT-NeoX 20B.
- In total, 5592 dyadic and triadic conversations were generated, one per topic combination in FITS. About 95% of the synthetic conversations stayed on the requested topic.
- Following DailyDialog, conversations are of three kinds: full start-to-finish dialogues, opening-to-middle excerpts, and other partial segments.
- Models fine-tuned on the synthetic data performed on par with those trained on DailyDialog/Topical Chat in interactive human evaluation and automatic metrics. The triadic data was compared against two human multi-party corpora.

## Interesting ideas
- **Recipe-prefixed demonstrations:** each example begins with a one-line template naming the speakers and topic, followed by background facts. Swapping in a new recipe steers generation cheaply, which is useful for any template-driven data synthesis.
- **Synthesis instead of augmentation:** a few carefully written examples replace a seed dataset sampled from some existing distribution. This fits languages or domains, such as Ukrainian dialogue, where no suitable corpus exists.
- **Random sampling of in-context examples:** drawing three demonstrations at random from a small pool for each prompt adds variety to the outputs without writing more seeds.
- **Two-level evaluation:** the synthetic data is judged both directly (rating its quality) and indirectly (interactive testing of models trained on it). This is a solid template for validating any generated training set.
- **Filling recipe slots from external sources:** the authors propose taking background details from existing annotations such as Persona Chat. Detailed backgrounds gave better results than generic ones, so the investment in richer slot content pays off.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 149 works · 48,852 citations
- [Amazon](https://md.research.elamur.ai/free/en/companies/amazon/) — Company, Seattle, United States · 70 works · 39,977 citations

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## Cite

Cite as: Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu and Dilek Hakkani-Tur. 2023. PLACES: Prompting Language Models for Social Conversation Synthesis. Findings of the Association for Computational Linguistics: EACL 2023.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chen2023places,
  title = {{PLACES: Prompting Language Models for Social Conversation Synthesis}},
  author = {Maximillian Chen, Alexandros Papangelis, Chenyang Tao, Seokhwan Kim, Andy Rosenbaum, Yang Liu, Zhou Yu and Dilek Hakkani-Tur},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2023},
  year = {2023},
  doi = {10.18653/v1/2023.findings-eacl.63},
  url = {https://doi.org/10.18653/v1/2023.findings-eacl.63}
}
```

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*Updated 24 October 2026*