@inproceedings{wang-etal-2026-improving,
title = "Improving Autoformalization Using Direct Dependency Retrieval",
author = "Wang, Shaoqi and
Yu, Lu and
Lou, Siwei and
Yan, Feng and
Yang, Chunjie and
Cui, Qing and
Zhou, Jun",
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.821/",
pages = "18023--18040",
ISBN = "979-8-89176-390-6",
abstract = "Statement autoformalization, a crucial first step in formal verification, aims to transform informal descriptions of math problems into machine-verifiable formal representations but remains a significant challenge. The core difficulty lies in the fact that existing language models hallucinate formal dependencies, including missing or incorrect definitions, lemmas, and theorems. Current dependency retrieval approaches exhibit poor precision and recall, and lack the scalability to leverage ever-growing public datasets. To bridge this gap, we propose a novel retrieval-augmented framework based on Direct Dependency Retrieval (DDR). DDR directly generates candidate formal dependencies from natural-language mathematical descriptions and verifies their existence in the formal library via an efficient Suffix Array Check (SAC). Built on a SAC-constructed dependency retrieval dataset of over 500,000 samples, a high-precision DDR model is fine-tuned and shown to significantly outperform state-of-the-art methods in both retrieval precision and recall, leading to superior advantage in the autoformalization tasks. SAC also contributes in assessing formalization difficulty and enabling explicit quantification of the hallucination in In-Context Learning (ICL)."
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<abstract>Statement autoformalization, a crucial first step in formal verification, aims to transform informal descriptions of math problems into machine-verifiable formal representations but remains a significant challenge. The core difficulty lies in the fact that existing language models hallucinate formal dependencies, including missing or incorrect definitions, lemmas, and theorems. Current dependency retrieval approaches exhibit poor precision and recall, and lack the scalability to leverage ever-growing public datasets. To bridge this gap, we propose a novel retrieval-augmented framework based on Direct Dependency Retrieval (DDR). DDR directly generates candidate formal dependencies from natural-language mathematical descriptions and verifies their existence in the formal library via an efficient Suffix Array Check (SAC). Built on a SAC-constructed dependency retrieval dataset of over 500,000 samples, a high-precision DDR model is fine-tuned and shown to significantly outperform state-of-the-art methods in both retrieval precision and recall, leading to superior advantage in the autoformalization tasks. SAC also contributes in assessing formalization difficulty and enabling explicit quantification of the hallucination in In-Context Learning (ICL).</abstract>
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%0 Conference Proceedings
%T Improving Autoformalization Using Direct Dependency Retrieval
%A Wang, Shaoqi
%A Yu, Lu
%A Lou, Siwei
%A Yan, Feng
%A Yang, Chunjie
%A Cui, Qing
%A Zhou, Jun
%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 wang-etal-2026-improving
%X Statement autoformalization, a crucial first step in formal verification, aims to transform informal descriptions of math problems into machine-verifiable formal representations but remains a significant challenge. The core difficulty lies in the fact that existing language models hallucinate formal dependencies, including missing or incorrect definitions, lemmas, and theorems. Current dependency retrieval approaches exhibit poor precision and recall, and lack the scalability to leverage ever-growing public datasets. To bridge this gap, we propose a novel retrieval-augmented framework based on Direct Dependency Retrieval (DDR). DDR directly generates candidate formal dependencies from natural-language mathematical descriptions and verifies their existence in the formal library via an efficient Suffix Array Check (SAC). Built on a SAC-constructed dependency retrieval dataset of over 500,000 samples, a high-precision DDR model is fine-tuned and shown to significantly outperform state-of-the-art methods in both retrieval precision and recall, leading to superior advantage in the autoformalization tasks. SAC also contributes in assessing formalization difficulty and enabling explicit quantification of the hallucination in In-Context Learning (ICL).
%U https://aclanthology.org/2026.acl-long.821/
%P 18023-18040
Markdown (Informal)
[Improving Autoformalization Using Direct Dependency Retrieval](https://aclanthology.org/2026.acl-long.821/) (Wang et al., ACL 2026)
ACL
- Shaoqi Wang, Lu Yu, Siwei Lou, Feng Yan, Chunjie Yang, Qing Cui, and Jun Zhou. 2026. Improving Autoformalization Using Direct Dependency Retrieval. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18023–18040, San Diego, California, United States. Association for Computational Linguistics.