By requesting access, you agree to use CSConDa responsibly, to not attempt to re-identify any individual or organization in the data, and to cite the CSConDa paper in any work that uses it.

CSConDa: Customer Support Conversations Dataset for Vietnamese

CSConDa is the first Vietnamese question answering dataset in the customer support domain. It contains over 9,000 QA pairs curated from real interactions between customers and human advisors. The data was collected and approved in collaboration with DooPage, a Vietnamese software company that serves 30,000 customers and 45,000 advisors through its multi-channel support platform.

The questions cover a wide range of service topics, from pricing inquiries and product availability to technical troubleshooting. They keep the informal style of real customer messages: abbreviations, typos, missing diacritics and code-switching. This makes CSConDa a realistic benchmark for Vietnamese LLMs in customer-facing applications.

Paper: Long S. T. Nguyen, Truong P. Hua, Thanh M. Nguyen, Toan Q. Pham, Nam K. Ngo, An X. Nguyen, Nghi D. M. Pham, Nghia H. Nguyen, and Tho T. Quan. A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support. 17th International Conference on Computational Collective Intelligence (ICCCI 2025).

TL;DR: CSConDa is a Vietnamese customer support QA benchmark built from real customer–advisor conversations. It contains 9,849 QA pairs labeled as General, Domain-Specific Simple or Domain-Specific Complex. It is suited to fine-tuning, question-type classification and benchmarking Vietnamese LLMs.

1. Dataset at a glance

Property	Value
QA pairs	9,849 (train 8,349 · test 1,500)
Source	Real customer–advisor conversations (DooPage)
Question types	General, Domain-Specific Simple, Domain-Specific Complex
Privacy	Personal and sensitive information is masked
Language	Vietnamese (informal, conversational)

2. Usage

CSConDa is a gated dataset. Before loading it:

Open this page while signed in to Hugging Face and submit the access form. Access is granted immediately.
Authenticate locally with hf auth login, or pass token=... to load_dataset.

from datasets import load_dataset

ds = load_dataset("ura-hcmut/Vietnamese-Customer-Support-QA")   # train / test

sample = ds["test"][0]
print(sample["question"])
print(sample["answer"])
print(sample["type"])

Intended uses:

Benchmarking: evaluate Vietnamese LLMs on realistic customer support questions using the test split.
Fine-tuning: adapt LLMs to customer support with the train split.
Classification: predict the question type from the customer message.

3. Data fields

Field	Type	Description
`question`	string	Customer message, kept in its original informal style
`answer`	string	Response written by a human support advisor
`type`	string	Question type: `General`, `Domain - Specific Simple` or `Domain - Specific Complex`

Example:

{
  "question": "phần chat trên page với shopê. ko nhìn thấy được đơn hàng khách đặt. có cách nào nhìn thấy kiểu khách đặt gì ko a.",
  "answer": "dạ hiện tại phần thông tin này shopee chưa truyền cho phần mềm ạ..",
  "type": "Domain - Specific Simple"
}

Privacy: names, phone numbers, emails, addresses, links and brand names are replaced with placeholders such as <tên>, <sđt>, <email>, <địa chỉ>, <liên kết> and <thương hiệu>.

4. Question types

Each pair is annotated following expert-guided guidelines.

Type	Definition	Characteristics	Example
General	Common conversational phrases unrelated to a specific topic, such as greetings, farewells or brief acknowledgments.	Short, with minimal semantic content and generic wording.	âu kê thank kiu e. (Okay, thank you.)
Domain-Specific Simple	Direct, fact-based questions that need a concise answer.	Concise, less dependent on context, often mentions service terms such as "Doopage", "chatbot" or "Zalo".	Báo giá giúp M nhé. Gọi M lúc 11h số này nef <số điện thoại>. C cần qly 8 page, 2 Zalo, 1 website, 1 YTB, 3-5 người dùng. (Please send me the pricing details. Call me at 11 AM at this number <phone number>. I need 8 pages, 2 Zalo accounts, 1 website and 1 YouTube channel for 3–5 users.)
Domain-Specific Complex	Questions that need detailed explanations, often troubleshooting with multiple steps.	Longer, with problem descriptions, error messages and technical terms.	à thế đây là teen maps. chứ đâu phải tên business đâu em. tên 1 địa chỉ map đó. Nhưng lsao mà gõ tên map vào đó được. trong khi bên trong cho phép add nhiều locations? Lỗi file này e ơi, ko down đc. Bên Zalo OA down bt. (Oh, so this is the name on Google Maps, not the business name. How can I enter a map name when multiple locations are allowed? This file is corrupted and cannot be downloaded. On Zalo OA, the download works fine.)

5. Splits and statistics

Type	Train	Test	All
General	3,023	500	3,523
Domain-Specific Simple	4,712	500	5,212
Domain-Specific Complex	614	500	1,114
Total	8,349	1,500	9,849

The test split is balanced across the three question types (500 each), so it supports a fair per-type comparison of models.

Average length (characters):

	Train	Test
Question	70.5	85.5
Answer	185.8	127.1

6. Evaluation

The paper benchmarks 11 lightweight open-source Vietnamese LLMs on the CSConDa test split. The evaluation combines automatic metrics with an in-depth syntactic analysis of the generated answers, covering accuracy, fluency and consistency. See the paper for the full results.

7. License

Released under the Apache License 2.0. Users must not attempt to re-identify any individual or organization in the data.

8. Citation

If you use CSConDa, please cite:

@inproceedings{10.1007/978-3-032-10202-7_33,
  title     = {A Benchmark Dataset and Evaluation Framework for Vietnamese Large Language Models in Customer Support},
  author    = {Nguyen, Long S. T. and Hua, Truong P. and Nguyen, Thanh M. and Pham, Toan Q. and Ngo, Nam K. and Nguyen, An X. and Pham, Nghi D. M. and Nguyen, Nghia H. and Quan, Tho T.},
  editor    = {Nguyen, Ngoc Thanh and Dinh Duc Anh, Vu and Kozierkiewicz, Adrianna and Nguyen Van, Sinh and Nunez, Manuel and Treur, Jan and Vossen, Gottfried},
  booktitle = {Advances in Computational Collective Intelligence},
  year      = {2026},
  publisher = {Springer Nature Switzerland},
  address   = {Cham},
  pages     = {481--496},
  isbn      = {978-3-032-10202-7}
}

9. Contact

For questions, issues or feedback, please contact Long S. T. Nguyen (long.nguyencse2023@hcmut.edu.vn), URA Research Group, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), VNU-HCM.

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