Note: Repo under development. We have released all data and evaluation code. We are now working on releasing fine-tuned models.
-
The WikiWebQuestions dataset can be found under directory
WikiWebQuestions; -
Training data for all models published in our paper can be found under directory
training_data; -
Prediction results for all models published in our paper can be found under directory
predicted_results.
The JSON file names correspond to the models in our paper in the following way:
-
bestrefers to the best model in our paper, i.e.,WikiSP (ours)in Section 6 tables; -
no_mention_oraclerefers to the model namedNo mentions, trained with Oracle NEDin Section 6.2 (Table 2); -
no_mention_refinedrefers to the model namedNo mentions, trained with ReFinEDin Section 6.2 (Table 2); -
original_query_formatrefers to the model namedOriginal SPARQLin Section 6.3 (Table 3).
To run evaluation on the dev set, prepare your prediction file in the same format as predicted_results/best.json, and supply it as the first parameter
for execute_predictions("predicted_results/best.json", "WikiWebQuestions/dev.json") in eval_predictions.py.
Then, run python eval_predictions.py.
If running on the test set, also change the second parameter to "WikiWebQuestions/test.json"
This section is under development.
Download the finetuned ReFiNED model by:
pip install https://github.com/amazon-science/ReFinED/archive/refs/tags/V1.zip
mkdir -p <your_directory>
curl https://almond-static.stanford.edu/research/qald/refined-finetune/config.json -o <your_directory>/config.json
curl https://almond-static.stanford.edu/research/qald/refined-finetune/model.pt -o <your_directory>/model.pt
curl https://almond-static.stanford.edu/research/qald/refined-finetune/precomputed_entity_descriptions_emb_wikidata_33831487-300.np -o <your_directory>/precomputed_entity_descriptions_emb_wikidata_33831487-300.np
and then check out run_refined.py for an example of inferencing with it.
If you have used data or code from this repository, please cite this paper:
@inproceedings{xu-etal-2023-fine,
title = "Fine-tuned {LLM}s Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over {W}ikidata",
author = "Xu, Silei and
Liu, Shicheng and
Culhane, Theo and
Pertseva, Elizaveta and
Wu, Meng-Hsi and
Semnani, Sina and
Lam, Monica",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.353",
pages = "5778--5791",
abstract = "While large language models (LLMs) can answer many questions correctly, they can also hallucinate and give wrong answers. Wikidata, with its over 12 billion facts, can be used to ground LLMs to improve their factuality. This paper presents WikiWebQuestions, a high-quality question answering benchmark for Wikidata. Ported over from WebQuestions for Freebase, it consists of real-world data with SPARQL annotation. This paper presents a few-shot sequence-to-sequence semantic parser for Wikidata. We modify SPARQL to use the unique domain and property names instead of their IDs. We train the parser to use either the results from an entity linker or mentions in the query. We fine-tune LLaMA by adding the few-shot training data to that used to fine-tune Alpaca. Our experimental results demonstrate the effectiveness of this methodology, establishing a strong baseline of 76{\%} and 65{\%} answer accuracy in the dev and test sets of WikiWebQuestions, respectively. By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96{\%} of the questions in dev. We also show that our method outperforms the state-of-the-art for the QALD-7 Wikidata dataset by 3.6{\%} in F1 score.",
}
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