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Paper Citation Record · LEDGER
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
As of 14 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 3 inbound Pith citation observations for arXiv:2412.15739.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
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Pith citing papers itemized under the disclosed page cap.
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External citation measurements
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Outbound references
Observation ebb77e48-b1c8-4bdf-8d58-0f8a340e7293 · outbound
This paper cites Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing
Reference 1
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This paper cites Ana- lyzing the behavior of visual question answering models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Ana- lyzing the behavior of visual question answering models
Reference 2
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Observation f7be4629-ad21-4184-a729-9bd028877d85 · outbound
This paper cites Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
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This paper cites Let there be a clock on the beach: Reducing object hal- lucination in image captioning.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Let there be a clock on the beach: Reducing object hal- lucination in image captioning
Reference 4
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This paper cites Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving
Reference 5
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This paper cites HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding
Reference 6
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This paper cites Calibrating deep neural networks by pairwise constraints.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Calibrating deep neural networks by pairwise constraints
Reference 7
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This paper cites InstructBLIP: Towards general-purpose vision-language models with instruction tuning.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models InstructBLIP: Towards general-purpose vision-language models with instruction tuning
Reference 8
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This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BERT: Pre-training of deep bidirectional trans- formers for language understanding
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This paper cites Hierarchi- cal neural story generation.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Hierarchi- cal neural story generation
Reference 10
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This paper cites Multi-modal hal- lucination control by visual information grounding.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Multi-modal hal- lucination control by visual information grounding
Reference 11
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Beam search strate- gies for neural machine translation
Reference 12
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This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Reference 13
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work
Reference 14
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This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing
Reference 15
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This paper cites A stitch in time saves nine: A train-time reg- ularizing loss for improved neural network calibration.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A stitch in time saves nine: A train-time reg- ularizing loss for improved neural network calibration
Reference 16
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This paper cites Dietterich.
Reference 17
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models The curious case of neural text degeneration
Reference 18
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models LoRA: Low-rank adaptation of large language models
Reference 19
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This paper cites Scaling up vision-language pre-training for image captioning.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Scaling up vision-language pre-training for image captioning
Reference 20
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Observation 23cf6aba-1040-401d-b3c3-a81e3bd20826 · outbound
This paper cites Movienet: A holistic dataset for movie un- derstanding.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Movienet: A holistic dataset for movie un- derstanding
Reference 21
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This paper cites Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation
Reference 22
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Hudson and Christopher D
Reference 23
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Langsuit-e: Controlling, planning, and interacting with large language models in embodied text environments
Reference 24
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work
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This paper cites Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding
Reference 26
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation
Reference 27
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This paper cites What does BERT with vision look at? In Proceedings of the 58th Annual Meeting of the Associa- tion for Computational Linguistics , pages 5265–5275, On- line, 2020.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models What does BERT with vision look at? In Proceedings of the 58th Annual Meeting of the Associa- tion for Computational Linguistics , pages 5265–5275, On- line, 2020
Reference 28
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Contrastive decoding: Open-ended text genera- tion as optimization
Reference 29
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Evaluating object hallucination in large vision- language models
Reference 30
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Vila: On pre-training for visual language models
Reference 31
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models TruthfulQA: Measuring how models mimic human falsehoods
Reference 32
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C
Reference 33
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models The devil is in the margin: Margin-based label smooth- ing for network calibration
Reference 34
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Class adaptive network calibration
Reference 35
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Visual instruction tuning
Reference 36
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Improved baselines with visual instruction tuning
Reference 37
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This paper cites SimCLS: A simple framework for contrastive learning of abstractive summarization.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models SimCLS: A simple framework for contrastive learning of abstractive summarization
Reference 38
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This paper cites Curved scene text detection via transverse and longitudinal sequence connection.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Curved scene text detection via transverse and longitudinal sequence connection
Reference 39
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This paper cites Roberta: A robustly optimized bert pretraining approach, 2019.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Roberta: A robustly optimized bert pretraining approach, 2019
Reference 40
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BRIO: Bringing order to abstractive summarization
Reference 41
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Soft augmentation for image classifica- tion
Reference 42
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This paper cites Dolphins: Multimodal language model for driving, 2023.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dolphins: Multimodal language model for driving, 2023
Reference 43
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Deepart: Learn- ing joint representations of visual arts
Reference 44
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Revisiting the calibration of modern neu- ral networks
Reference 45
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VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Confidence-aware learning for deep neural net- works
Reference 46
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This paper cites When does label smoothing help? In Advances in Neu- ral Information Processing Systems.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models When does label smoothing help? In Advances in Neu- ral Information Processing Systems
Reference 47
- Resolution
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- raw_fallback, observed 2026-08-11T11:11:08.974344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c31ccbc7-2cf3-44b0-aa9e-922d22d009db · outbound
This paper cites Cooper, and Milos Hauskrecht.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Cooper, and Milos Hauskrecht
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.940570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9e1cd706-5266-4032-a811-03750bb6d645 · outbound
This paper cites Rankmixup: Ranking-based mixup training for net- work calibration.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Rankmixup: Ranking-based mixup training for net- work calibration
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.921206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a13db97e-dd97-40a1-8f26-20601727d037 · outbound
This paper cites Gpt-4 technical report.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Gpt-4 technical report
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.902280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e15b0d9a-80c7-46e7-b733-ef28d27e9e86 · outbound
This paper cites Learning transferable visual models from natural language supervision.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Learning transferable visual models from natural language supervision
Reference 51
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3acdf735-75e1-45fb-a14a-268ef5cf5704 · outbound
This paper cites SummaR- eranker: A multi-task mixture-of-experts re-ranking frame- work for abstractive summarization.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models SummaR- eranker: A multi-task mixture-of-experts re-ranking frame- work for abstractive summarization
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.858585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation f4b95d91-835a-4f64-be5a-a9bdcc367364 · outbound
This paper cites Distributionally robust ensemble of lottery tickets towards calibrated sparse network training.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Distributionally robust ensemble of lottery tickets towards calibrated sparse network training
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.832857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 2bbe0bbc-6227-4a58-8a7c-378ea6abb84a · outbound
This paper cites A-okvqa: A benchmark for visual question answering using world knowl- edge.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A-okvqa: A benchmark for visual question answering using world knowl- edge
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.816080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation a90775c8-d2fe-454e-98d3-b0f77f8962cb · outbound
This paper cites REPLUG: Retrieval-augmented black-box language models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models REPLUG: Retrieval-augmented black-box language models
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.799026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation da305622-9b1d-414d-b410-7e67fac6f2b8 · outbound
This paper cites Videobert: A joint model for video and language representation learning.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Videobert: A joint model for video and language representation learning
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.774766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 96c70ff4-068c-4823-ad6b-f72242ebbcb9 · outbound
This paper cites Sq-llava: Self-questioning for large vision-language assistant.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Sq-llava: Self-questioning for large vision-language assistant
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.748964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation d309cf41-699b-4574-b85d-a5918e35badf · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:11:08.027836Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9b159bed-56ca-4976-8033-f327b97bb3a5 · outbound
This paper cites A closer look at the robustness of contrastive language-image pre-training (clip).
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A closer look at the robustness of contrastive language-image pre-training (clip)
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.725549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bc871a3a-5abf-4ec7-ad48-b29d5d5e6d42 · outbound
This paper cites An empirical study into what matters for calibrating vision-language models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models An empirical study into what matters for calibrating vision-language models
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.703552Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bfd9d476-06f8-4625-b7f0-9049d03a6511 · outbound
This paper cites GIT: A generative image-to-text transformer for vision and language.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models GIT: A generative image-to-text transformer for vision and language
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.685635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation afbc4c99-980c-4a8c-82e2-293803c20a8b · outbound
This paper cites ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models
Reference 62
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ce95740-d65c-4bd9-b42d-660870476e79 · outbound
This paper cites Editable scene simulation for autonomous driving via collaborative llm-agents.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Editable scene simulation for autonomous driving via collaborative llm-agents
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.667098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 81a2498c-861f-445c-a08e-75e8b20bfc81 · outbound
This paper cites Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.641238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e7fa6ad8-76fa-4214-8694-0cd71000b2b6 · outbound
This paper cites Precedent-enhanced legal judgment prediction with LLM and domain-model collaboration.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Precedent-enhanced legal judgment prediction with LLM and domain-model collaboration
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.614753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c2434f08-8275-4e78-b2a2-9ce643d52a3e · outbound
This paper cites Martindale, and Marine Carpuat.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Martindale, and Marine Carpuat
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.589776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation eb4e3cd2-f926-4301-908a-1515592767b0 · outbound
This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:11:08.077580Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 07866df3-59af-4006-b9fe-7f6005c1a38d · outbound
This paper cites Mul- timodal healthcare ai: Identifying and designing clinically relevant vision-language applications for radiology.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mul- timodal healthcare ai: Identifying and designing clinically relevant vision-language applications for radiology
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.558671Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation fb3fab50-01aa-41af-941c-b55f2870ba76 · outbound
This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Woodpecker: Hallucination Correction for Multimodal Large Language Models
Reference 69
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:11:08.091154Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be99acf2-2a7f-4ab5-8a2b-938749c71590 · outbound
This paper cites Dauphin, and David Lopez-Paz.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dauphin, and David Lopez-Paz
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.531259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 13aa1e1c-5b3d-4ac4-8264-1250fb04766a · outbound
This paper cites What if the tv was off? examining counterfactual reasoning abilities of multi-modal language models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models What if the tv was off? examining counterfactual reasoning abilities of multi-modal language models
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.507251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7d2acbd9-d772-43e8-8069-ffa3d721984b · outbound
This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:11:08.110730Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d076d16-8717-46cd-a5bf-685aad026328 · outbound
This paper cites Calibrating sequence likelihood improves conditional language genera- tion.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Calibrating sequence likelihood improves conditional language genera- tion
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.477783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1fd10b15-7c81-4a75-bdb4-26d6d42334d3 · outbound
This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Judging llm-as-a-judge with mt-bench and chatbot arena
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.457012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 9d1d5b7d-4ccc-43b3-9569-304625f7ef61 · outbound
This paper cites Learning deep features for scene recognition using places database.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Learning deep features for scene recognition using places database
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.428399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 3ae03b19-753c-4df1-a5c8-39043de5d5f1 · outbound
This paper cites Detecting hallucinated content in conditional neural sequence generation.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Detecting hallucinated content in conditional neural sequence generation
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T11:11:08.403825Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 024b06c8-d5ab-49cb-92b4-adac92ce81a5 · outbound
This paper cites Jpeg Compression.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Jpeg Compression
Reference 77
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-11T11:11:08.372299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 7c32e135-9339-44e5-95ee-75f3522c9ea7 · outbound
This paper cites an unresolved cited work.
VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:11:07.867924Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 09f18509-1557-4199-8b5d-7d55abb7b0bd · inbound
When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs cites this paper.
When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T01:57:05.219770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation dd68306d-7301-41a3-a1eb-b4d74a1252be · inbound
Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation cites this paper.
Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:14:18.714212Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e30a4de1-ce8b-4b71-b725-ab52c795d6a1 · inbound
Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation cites this paper.
Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T07:05:28.652874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.