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⚄ quintd

Data-to-text generation with LLMs using ad-hoc datasets.

Project website: https://d2t-llm.github.io

🎈 [NEW] Here is our annotation framework as an open-source library: https://github.com/kasnerz/factgenie/

Datasets

Quintd is a tool for downloading structured data from five data-to-text generation tasks. Each task uses data from a distinct domain:

Domain Task Task Description Source Format
🌦️ Weather openweather Generating a weather forecast from weather data. OpenWeather JSON
📱 Technology gsmarena Describing a product based on its attributes. GSMArena JSON
🏒 Sport ice_hockey Describing an outcome of an ice-hockey game. RapidAPI JSON
📈 Health owid Generating a caption for a time series. OurWorldInData CSV
🌐 World facts wikidata Describing entities and relations in a knowledge graph. Wikidata Markdown

We use the data to evaluate semantic accuracy of data-to-text generation outputs.

As the data comes without reference outputs, the evaluation is done using referenceless metrics (checking whether the outputs are grounded in the input data).

Preliminaries

The code is tested with Python 3.10. Make sure to install the required packages first:

pip install -r requirements.txt

Project Overview

The code enables to replicate the experiments described in our paper:

Zdeněk Kasner & Ondřej Dušek: Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation. In: Proceedings of the 62th Annual Meeting of the Association for Computational Linguistics (ACL 2024).

. The experiments consist of three stages:

  • Collecting structured data using Quintd.
  • Generating data-based reports using LLMs.
  • Annotating outputs using LLMs and human annotators.

Collecting structured data

Quintd is a tool for downloading "ad hoc" test sets. The goal is to have evaluation data not included in the training data of the LLMs.

There are two ways that Quintd achieves that:

  • downloading up-to-date data (for the weather forecasts, partially also for products and game results),
  • selecting different examples based on the random seed.

The recommended approach is to download a fresh test set for each set of experiments.

How-to

Here is how to generate a new dataset with Quintd:

python data/generate_dataset.py -d [DOMAIN] -n [EXAMPLES] -r [SEED] -o [OUT_DIR]

A basic setting which will generate a small dataset (10 examples per domain) with the random seed 7331:

NUM_EXAMPLES=10
SEED=7331

python data/generate_dataset.py -n $NUM_EXAMPLES -r $SEED

In our experiments, we generated one such dataset which we dubbed Quintd-1. The dataset is available in data/quintd-1/data.

The following code will try to replicate the data collection for Quintd-1 (up to some differences in API responses):

python data/generate_dataset.py --replicate

Generating data-based reports

Data-to-text generation requires having access to a LLM through an API. The code we provide here is tested with:

It should be fairly easy to connect the code with any other OpenAI-like API.

How-to

Here is how to generate outputs for a particular domain, model, and setup:

python model/generate.py -d [DOMAIN] -s [SETUP] -m [MODEL] -p [DATASET_PATH] -a [API_URL]

Example:

export TG_WEBUI_API_URL="http://tdll-8gpu1.ufal.hide.ms.mff.cuni.cz:5000"
python model/generate.py -d ice_hockey -s direct -m zephyr -p data/quintd-1 -a $TG_WEBUI_API_URL

You can also run the experiments with a single command:

python run_experiments.py

See the config file model/setups/direct.yaml for the model prompt and other hyperparameters.

The generated outputs for Quintd-1 are available in data/quintd-1/outputs.

Annotating outputs

We published our framework for annotating errors in LLM outputs as a standalone project 🎉

👉️ factgenie 👈️

We highly recommended your to use factgenie for your own experiments instead of the code in this repository.

How to

Here is how to replicate the LLM annotation process (without factgenie, using only the code in the repository).

For generating outputs for a particular domain, model, and setup:

python evaluation/evaluate.py -d [DOMAIN] -s [SETUP] -m [MODEL] -p [DATASET_PATH]

Example:

python evaluation/evaluate.py -d ice_hockey -s direct -m zephyr -p data/quintd-1

You can also run the evaluation with a single command:

python run_gpt4_eval.py

The error annotations for Quintd-1 are available in data/quintd-1/annotations.

Note that error annotation using GPT-4 requires access to the OpenAI API.

Code structure overview

Cite us

If you build upon our work, please cite our ACL paper:

@inproceedings{kasner2024beyond,
    title = "Beyond Traditional Benchmarks: Analyzing Behaviors of Open {LLM}s on Data-to-Text Generation",
    author = "Kasner, Zden{\v{e}}k  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.651",
    pages = "12045--12072",
}

Acknowledgements

This work was co-funded by the European Union (ERC, NG-NLG, 101039303).

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