@inproceedings{cheng-etal-2026-graphsynth,
title = "{G}raph{S}ynth: Resolving the Diversity-Reliability Trade-off with Probabilistic Factor Graphs",
author = "Cheng, Zehua and
Dai, Wei and
Sun, Jiahao and
Lukasiewicz, Thomas",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1969/",
pages = "42527--42543",
ISBN = "979-8-89176-390-6",
abstract = "The large language models offer a scaleable solution for the generation of synthetic data faced with a trade-off between maintaining the diversity of generation and achieving factually accurate results. This paper introduces Graphsynth, a framework which leverages a probabilistic factor graph modeling the universe of attributes. The framework leverages a high-level schema mapping compiled into efficient hard masks during the decoding phase for maintaining the syntactic truth and a span-synchronized verifier for dismissing logical contradictions at the decode time. The experiments conducted on biomedical, legal, and generic domains show that the method outperforms the state-of-the-art baselines with a structural integrity approaching perfection, a coverage of around 94{\%} attributes on the factor graph solution, and a boost in performance on downstream tasks such as +17.9{\%} on TruthfulQA."
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%0 Conference Proceedings
%T GraphSynth: Resolving the Diversity-Reliability Trade-off with Probabilistic Factor Graphs
%A Cheng, Zehua
%A Dai, Wei
%A Sun, Jiahao
%A Lukasiewicz, Thomas
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cheng-etal-2026-graphsynth
%X The large language models offer a scaleable solution for the generation of synthetic data faced with a trade-off between maintaining the diversity of generation and achieving factually accurate results. This paper introduces Graphsynth, a framework which leverages a probabilistic factor graph modeling the universe of attributes. The framework leverages a high-level schema mapping compiled into efficient hard masks during the decoding phase for maintaining the syntactic truth and a span-synchronized verifier for dismissing logical contradictions at the decode time. The experiments conducted on biomedical, legal, and generic domains show that the method outperforms the state-of-the-art baselines with a structural integrity approaching perfection, a coverage of around 94% attributes on the factor graph solution, and a boost in performance on downstream tasks such as +17.9% on TruthfulQA.
%U https://aclanthology.org/2026.acl-long.1969/
%P 42527-42543
Markdown (Informal)
[GraphSynth: Resolving the Diversity-Reliability Trade-off with Probabilistic Factor Graphs](https://aclanthology.org/2026.acl-long.1969/) (Cheng et al., ACL 2026)
ACL