@inproceedings{guo-etal-2025-dsvd,
title = "{DSVD}: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models",
author = "Guo, YiQiu and
Yang, Yuchen and
Chen, Zhe and
Wang, Pingjie and
Liao, Yusheng and
Zhang, Ya and
Wang, Yanfeng and
Wang, Yu",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1050/",
doi = "10.18653/v1/2025.emnlp-main.1050",
pages = "20789--20808",
ISBN = "979-8-89176-332-6",
abstract = "The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. Existing solutions either underutilize models' self-correction with preemptive strategies or use costly post-hoc verification. To further explore the potential of real-time self-verification and correction, we present Dynamic Self-Verify Decoding (DSVD), a novel decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. DSVD integrates two key components: (1) parallel self-verification architecture for continuous quality assessment, (2) dynamic rollback mechanism for targeted error recovery. Extensive experiments across five benchmarks demonstrate DSVD{'}s effectiveness, achieving significant improvement in truthfulness (Quesetion-Answering) and factual accuracy (FActScore). Results show the DSVD can be further incorporated with existing faithful decoding methods to achieve stronger performance. Our work establishes that real-time self-verification during generation offers a viable path toward more trustworthy language models without sacrificing practical deployability."
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<abstract>The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. Existing solutions either underutilize models’ self-correction with preemptive strategies or use costly post-hoc verification. To further explore the potential of real-time self-verification and correction, we present Dynamic Self-Verify Decoding (DSVD), a novel decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. DSVD integrates two key components: (1) parallel self-verification architecture for continuous quality assessment, (2) dynamic rollback mechanism for targeted error recovery. Extensive experiments across five benchmarks demonstrate DSVD’s effectiveness, achieving significant improvement in truthfulness (Quesetion-Answering) and factual accuracy (FActScore). Results show the DSVD can be further incorporated with existing faithful decoding methods to achieve stronger performance. Our work establishes that real-time self-verification during generation offers a viable path toward more trustworthy language models without sacrificing practical deployability.</abstract>
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%0 Conference Proceedings
%T DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models
%A Guo, YiQiu
%A Yang, Yuchen
%A Chen, Zhe
%A Wang, Pingjie
%A Liao, Yusheng
%A Zhang, Ya
%A Wang, Yanfeng
%A Wang, Yu
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F guo-etal-2025-dsvd
%X The reliability of large language models remains a critical challenge, particularly due to their susceptibility to hallucinations and factual inaccuracies during text generation. Existing solutions either underutilize models’ self-correction with preemptive strategies or use costly post-hoc verification. To further explore the potential of real-time self-verification and correction, we present Dynamic Self-Verify Decoding (DSVD), a novel decoding framework that enhances generation reliability through real-time hallucination detection and efficient error correction. DSVD integrates two key components: (1) parallel self-verification architecture for continuous quality assessment, (2) dynamic rollback mechanism for targeted error recovery. Extensive experiments across five benchmarks demonstrate DSVD’s effectiveness, achieving significant improvement in truthfulness (Quesetion-Answering) and factual accuracy (FActScore). Results show the DSVD can be further incorporated with existing faithful decoding methods to achieve stronger performance. Our work establishes that real-time self-verification during generation offers a viable path toward more trustworthy language models without sacrificing practical deployability.
%R 10.18653/v1/2025.emnlp-main.1050
%U https://aclanthology.org/2025.emnlp-main.1050/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1050
%P 20789-20808
Markdown (Informal)
[DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models](https://aclanthology.org/2025.emnlp-main.1050/) (Guo et al., EMNLP 2025)
ACL
- YiQiu Guo, Yuchen Yang, Zhe Chen, Pingjie Wang, Yusheng Liao, Ya Zhang, Yanfeng Wang, and Yu Wang. 2025. DSVD: Dynamic Self-Verify Decoding for Faithful Generation in Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20789–20808, Suzhou, China. Association for Computational Linguistics.