👻 Hallucination Detection¶

📷 CVPR2025 · 9 paper notes

📌 Same area in other venues: 📷 CVPR2026 (33) · 🔬 ICLR2026 (40) · 💬 ACL2026 (28) · 🧪 ICML2026 (21) · 🤖 AAAI2026 (15) · 🧠 NeurIPS2025 (17)

🔥 Top topics: Multimodal/VLM ×2

3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination: This work constructs 3D-GRAND, the first million-scale densely grounded 3D scene-language dataset (40K scenes, 6.2M instructions), and proposes the 3D-POPE hallucination evaluation benchmark. It demonstrates that densely grounded instruction tuning significantly enhances the grounding capability of 3D-LLMs and reduces hallucinations, while also showcasing effective sim-to-real transfer.
Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception: This paper proposes Antidote—a unified, synthetic data-driven post-training framework that enables model self-correction by injecting factual priors into prompts, decoupling hallucination mitigation as a preference optimization problem. It improves CP-Bench by over 50% on the LLaVA series, increases POPE by 1.8-3.3%, and reduces CHAIR/SHR by 30-50% without suffering from catastrophic forgetting.
HalLoc: Token-Level Localization of Hallucinations for Vision Language Models: This work proposes HalLoc, a token-level hallucination annotation dataset with 155K samples covering three categories of tasks: VQA, instruction following, and image captioning. Based on this, a lightweight hallucination detection model named HalLocalizer is trained, which can be integrated into existing VLMs in a plug-and-play manner to achieve real-time probabilistic hallucination detection without sacrificing efficiency.
Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding: Through extensive experiments, this paper reveals the hybrid nature of hallucination causes in LVLMs—different samples and different generation steps face different flags of hallucination challenges. Consequently, the Octopus framework is proposed, which utilizes a learnable decision token and a transformer block to adaptively select the most appropriate contrastive decoding (CD) strategy at each generation step. Optimized via DPO, Octopus outperforms existing CD methods across four benchmarks.
ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models: This paper proposes the ODE (Open-set Dynamic Evaluation) protocol, which models real-world object concepts and their distribution associations using a graph structure. It dynamically extracts concept combinations and generates synthetic test images to realize open-set, continuously updated multimodal hallucination evaluation, effectively avoiding the data contamination issues potentially present in current static benchmarks.
One Token, Two Fates: A Unified Framework via Vision Token Manipulation Against MLLMs Hallucination: Proposes the first unified, training-free framework for mitigating MLLM hallucinations, operating synergistically within the hidden representation layers based on the dual roles of vision tokens—enhancement (SVC) and suppression (CRC). It improves POPE accuracy by ~2% on LLaVA-1.5 with only a 1.06× increase in inference latency.
PhD: A ChatGPT-Prompted Visual Hallucination Evaluation Dataset: This paper proposes PhD, a large-scale visual hallucination evaluation dataset constructed with the assistance of ChatGPT. It contains 14K+ everyday images, 750 counter-commonsense images, and 102K VQA triplets. Through 4 evaluation modes \(\times\) 5 visual tasks, it systematically evaluates the hallucination issues of multimodal large language models (MLLMs), far exceeding existing benchmarks in scale and difficulty.
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding: FarSight is proposed as a plug-and-play, training-free decoding strategy. It introduces attention registers into the upper triangle matrix of the causal mask to absorb excessive attention on anomalous tokens, and designs positional awareness encoding with diminishing masking rates to enhance information propagation for distant visual tokens, thereby effectively mitigating initial and snowball hallucinations in Multimodal Large Language Models (MLLMs).
Stop Learning It All to Mitigate Visual Hallucination, Focus on the Hallucination Target: Proposes TL-DPO (Target-Learning DPO), which limits traditional full-sentence preference learning to the target chunk where hallucination occurs and the corresponding image region. By excluding irrelevant signals through target generation loss and target condition loss, it reduces CHAIR_s on LLaVA-1.5 from 66.8 to 20.1, while improving LLaVA-Bench from 63.4 to 71.2.