qstn 0.5.1

A Modular Framework for Robust Questionnaire Inference with Large Language Models

QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models

QSTN is a Python framework designed to facilitate the creation of robust inference experiments with Large Language Models based around questionnaires. It provides a full pipeline from perturbation of prompts, to choosing Response Generation Methods, inferencing and finally parsing of the output. QSTN supports both local inference with vllm and remote inference via the OpenAI API.

Detailed information and guides are available in our documentation. Tutorial notebooks can also be found in this repository.

Installation

Make sure that the python version in your environment is at least 3.12

We support two type of installations:
1. The base version, which only installs the dependencies neccessary to use the OpenAI API.
2. The full version, which supports both API and local inference via vllm.

To install both of these version you can use pip or uv.

The base version can be installed with the following command:

pip install qstn

The full version can be installed with this command:

pip install "qstn[vllm]"

You can also install this package from source:

pip install git+https://github.com/dess-mannheim/QSTN.git

Getting Started

Below you can find a minimum working example of how to use QSTN. It can be easily integrated into existing projects, requiring just three function calls to operate. Users familiar with vllm or the OpenAI API can use the same Model/Client calls and arguments. In this example reasoning and the generated response are automatically parsed. For more elaborate examples, see the tutorial notebooks.

import qstn
import pandas as pd
from vllm import LLM

# 1. Prepare questionnaire and persona data
questionnaires = pd.read_csv("hf://datasets/qstn/ex/q.csv")
personas = pd.read_csv("hf://datasets/qstn/ex/p.csv")
prompt = (
    f"Please tell us how you feel about:\n"
    f"{qstn.utilities.placeholder.PROMPT_QUESTIONS}"
)
interviews = [
    qstn.prompt_builder.LLMPrompt(
        questionnaire_source=questionnaires,
        system_prompt=persona,
        prompt=prompt,
    ) for persona in personas.system_prompt]

# 2. Run Inference
model = LLM("Qwen/Qwen3-4B", max_model_len=5000)
results = qstn.survey_manager.conduct_survey_single_item(
    model, interviews, max_tokens=500
)

# 3. Parse Results
parsed_results = qstn.parser.raw_responses(results)

Citation

If you find QSTN useful in your work, please cite our paper:

@inproceedings{kreutner-etal-2026-qstn,
    title = "{QSTN}: A Modular Framework for Robust Questionnaire Inference with Large Language Models",
    author = "Kreutner, Maximilian  and
      Rupprecht, Jens  and
      Ahnert, Georg  and
      Salem, Ahmed  and
      Strohmaier, Markus",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-demo.37/",
    doi = "10.18653/v1/2026.eacl-demo.37",
    pages = "537--549",
    ISBN = "979-8-89176-382-1"
}