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arXivArizona StateHallucination & factuality

Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach

CLEAR enables LLMs to self-identify and correct errors via concept-specific sparse subnetworks without tuning, improving deployment accountability.

Zhen Wah Tan, Jie Peng, Tianlong Chen, Huan Liu

Published Mar 8, 2024 · 3 citations

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5/20 AI panelreviewers recommend it

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AI panel: 5 of 20 reviewers recommend it
lenient 4/5
medium 1/10
strict 0/5
72%Highly rated
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ACL 2024U WashingtonU California, BerkeleyHong Kong University of Science Carnegie MellonHallucination & factuality

Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Multi-LLM collaboration detects knowledge gaps to make LLMs abstain from wrong answers instead of hallucinating.

Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding and 2 more

Published 2024 · 37 citations

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8/21 AI panelreviewers recommend it

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AI panel: 8 of 21 reviewers recommend it
lenient 5/5
medium 3/11
strict 0/5
71%Highly rated
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EMNLP 2024U WashingtonThe University of Texas at AustiMicrosoftHallucination & factuality

Teaching LLMs to Abstain across Languages via Multilingual Feedback

Multilingual feedback teaches LLMs to abstain from answering in low-resource languages and improves cross-lingual abstention without degrading performance.

Shangbin Feng, Weijia Shi, Yike Wang, Wenxuan Ding and 5 more

Published 2024 · 4 citations

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6/20 AI panelreviewers recommend it

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5
67%Highly rated
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NeurIPS 2026SpotlightU Technology SydneySingapore ManagementU Wisconsin-Madison" University of Technology,Hallucination & factuality

CHORD: Cross-Model Hallucination Detection via Relational Graph Discrimination

Yongxin Deng, Zhen Fang, Guansong Pang, Sharon Li and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026Samsung ResearchKAISTYonseiKorea Advanced Institute of ScieHallucination & factuality

Beyond Uniform Detection: Adaptive Hallucination Detection for RAG Across Response Regimes

Jungwuk Park, Sejong Ryu, Jy-yong Sohn, Jaekyun Moon

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
57%Worth a look
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NeurIPS 2026TechnionTechnion & Deci.AIHallucination & factuality

When Should LLMs Be Less Specific? Selective Abstraction for Reliable Long-Form Text Generation

Shani Goren, Ido Galil, Ran El-Yaniv

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
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NeurIPS 2026MBZUAIMohamed bin Zayed University of ZhejiangWuhanHallucination & factuality

When Copying Is Hard: Copy-Constrained Decoding for Exact Span Reproduction

Jinghui Zhang, Lang Gao, Zongfang Liu, Ruihong Zeng and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 0/5
medium 0/10
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45%Niche pick
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NeurIPS 2026City University of Hong KongTencentCityWestlakeHallucination & factuality

Feeling of Knowing in Large Language Models

Zichuan Fu, Xian Wu, Jingtong Gao, Wenlin Zhang and 8 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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medium 0/10
strict 0/5
57%Worth a look
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NeurIPS 2026U HelsinkiHallucination & factuality

The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation

Jorma Valjakka, Juhani Kivimäki, Juha Mylläri, Jukka K Nurminen

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026Royal Melbourne Institute of TecRMITHallucination & factuality

Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention

Jing Ren, Bowen Li, Ziqi Xu, Xuechao Yang and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026MonashNortheasternU Minnesota - Twin CitiesUC Santa CruzHallucination & factuality

Rethinking in Spikes: Mitigating Hallucinations in MDLMs with Step-Aware Decoding

Zhongxing Xu, Zhonghua Wang, Zhe Qian, Shiyan Su and 8 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026U CambridgeBocconiHallucination & factuality

Large Language Model Failures from Hallucination to Homogenization Are Different Facets of Miscalibration

Tiancheng Hu, Caiqi Zhang, Dirk Hovy, Nigel Collier

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
45%Niche pick
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NeurIPS 2026Virginia StateHallucination & factuality

Shared Truth: Emergent Truth Properties in Large Language Models via Heterogeneous Injection-based Transfer

Isaiah Freeman, Joed Ngangmeni

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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lenient 0/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026RASResearch Center for Trusted AI, Hallucination & factuality

PLLS-CP: Unsupervised Hallucination Detection with Layer Selection and Cross-Domain Conformal Guarantees

Vladislav Tsvelenev, Maxim Ryndin

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026Qualcomm Inc, QualCommQualcomm AI ResearchAmazonUCSB/Google DeepMindQualcommHallucination & factuality

Alleviating Hallucination with Training-Free Uncertainty-Guided Steering

Litian Liu, Yubing Jian, Qiqi Hou, Reza Pourreza and 4 more

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
57%Worth a look
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NeurIPS 2026Patsnap Information Technology (PatSnapZhejiangHallucination & factuality

SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection

Jiawei Dong, Zilong Bai, Pengtian Zhu, Peng Zhou

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
67%Highly rated
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NeurIPS 2026National University of Defense TNaional University of Defense TeSchool of Computer Science, NatiHallucination & factuality

Overthinking as a Symptom of Knowledge Conflict: Understanding and Detecting LLM's Hallucinations in Retrieval-Augmented Question Answering

Zhihua Wen, Li Hao, Zhiliang Tian, Zhizhao Liu and 3 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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lenient 1/5
medium 1/10
strict 0/5
67%Highly rated
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NeurIPS 2026Nanjing University of Science anAnhui University of TechnologyHong Kong PolytechnicU LuxemburgData61, CSIROHallucination & factuality

Invisible Ink, Visible Lies: How Production Watermarking Causes LLMs to Hallucinate

Haocheng Ye, Aoting Hu, Xinwei Zhang, Xunzhu Tang and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 1/5
medium 0/10
strict 1/5
78%Highly rated
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NeurIPS 2026The Hong Kong University of ScieJilinHKUST-GZHong Kong University of Science Hallucination & factuality

Geometry-Calibrated Conformal Abstention for Language Models

Conformal Abstention uses geometry-calibrated confidence to decide abstention with finite-sample correctness guarantees, achieving 75% conditional correctness.

Rui Xu, Yi Chen, Sihong Xie, Hui Xiong

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 0/5
88%Must read
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NeurIPS 2026Shanghai JiaotongTencentHuawei Technologies Ltd.The Hong Kong University of ScieU Edinburgh, University of EdinbHallucination & factuality

BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation

BALTO applies balanced token-level credit assignment to mitigate LLM hallucinations by redistributing probability from unsupported to faithful content, outperforming response-level methods on faithfulness benchmarks.

Ning Li, Zixuan Guo, Yan Xu, Wenbo Fei and 6 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 9/10
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