@inproceedings{liu-etal-2026-gated,
title = "Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification",
author = "Liu, Zeyang and
Xue, Jingfeng and
Fu, Jiarun and
Du, Wenbiao and
Yang, Xiuqi and
Fu, Yifeng and
Chen, Junbao and
Wang, Yong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.84/",
pages = "1939--1952",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) often generate specious hallucinations, creating an urgent need for post-hoc factuality verification against reliable evidence. Existing verification methods incur substantial computational overhead due to joint evidence{--}claim encoding. While bi-encoders offer a highly efficient alternative, they struggle with asymmetric entailment and are severely vulnerable to contextual noise. To address these challenges, we propose Gated Residual Discrepancy (GRD), a highly efficient bi-encoder framework for factuality verification. To explicitly capture asymmetric entailment, GRD pioneers a difference-dominant paradigm, extracting dual-view vector differences. To mitigate the vulnerability to contextual noise, GRD employs an adaptive gating mechanism that dynamically filters contextual noise, preventing large semantic backgrounds from masking critical errors, thereby enabling highly sensitive verification over broad evidence contexts. Experiments across various benchmarks show GRD achieves 79.99{\%} average Macro-F1, trailing the strongest cross-encoder baseline by only 1.15 points while delivering a 23.55$\times$ speedup with only a fraction of the computational FLOPs."
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<abstract>Large Language Models (LLMs) often generate specious hallucinations, creating an urgent need for post-hoc factuality verification against reliable evidence. Existing verification methods incur substantial computational overhead due to joint evidence–claim encoding. While bi-encoders offer a highly efficient alternative, they struggle with asymmetric entailment and are severely vulnerable to contextual noise. To address these challenges, we propose Gated Residual Discrepancy (GRD), a highly efficient bi-encoder framework for factuality verification. To explicitly capture asymmetric entailment, GRD pioneers a difference-dominant paradigm, extracting dual-view vector differences. To mitigate the vulnerability to contextual noise, GRD employs an adaptive gating mechanism that dynamically filters contextual noise, preventing large semantic backgrounds from masking critical errors, thereby enabling highly sensitive verification over broad evidence contexts. Experiments across various benchmarks show GRD achieves 79.99% average Macro-F1, trailing the strongest cross-encoder baseline by only 1.15 points while delivering a 23.55\times speedup with only a fraction of the computational FLOPs.</abstract>
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%0 Conference Proceedings
%T Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification
%A Liu, Zeyang
%A Xue, Jingfeng
%A Fu, Jiarun
%A Du, Wenbiao
%A Yang, Xiuqi
%A Fu, Yifeng
%A Chen, Junbao
%A Wang, Yong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-gated
%X Large Language Models (LLMs) often generate specious hallucinations, creating an urgent need for post-hoc factuality verification against reliable evidence. Existing verification methods incur substantial computational overhead due to joint evidence–claim encoding. While bi-encoders offer a highly efficient alternative, they struggle with asymmetric entailment and are severely vulnerable to contextual noise. To address these challenges, we propose Gated Residual Discrepancy (GRD), a highly efficient bi-encoder framework for factuality verification. To explicitly capture asymmetric entailment, GRD pioneers a difference-dominant paradigm, extracting dual-view vector differences. To mitigate the vulnerability to contextual noise, GRD employs an adaptive gating mechanism that dynamically filters contextual noise, preventing large semantic backgrounds from masking critical errors, thereby enabling highly sensitive verification over broad evidence contexts. Experiments across various benchmarks show GRD achieves 79.99% average Macro-F1, trailing the strongest cross-encoder baseline by only 1.15 points while delivering a 23.55\times speedup with only a fraction of the computational FLOPs.
%U https://aclanthology.org/2026.emnlp-main.84/
%P 1939-1952
Markdown (Informal)
[Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification](https://aclanthology.org/2026.emnlp-main.84/) (Liu et al., EMNLP 2026)
ACL
- Zeyang Liu, Jingfeng Xue, Jiarun Fu, Wenbiao Du, Xiuqi Yang, Yifeng Fu, Junbao Chen, and Yong Wang. 2026. Gated Residual Discrepancy for Efficient Bi-Encoder Factuality Verification. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 1939–1952, Budapest, Hungary. Association for Computational Linguistics.