Paper Citation Record · LEDGER

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis

As of 24 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2412.02946.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.02946 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:01:32.375346Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T01:18:13.657975Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T19:06:02.496869Z

Reference resolution

54 of 54 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4bc0106-fdbe-40b5-82e0-f07591019960 · outbound

This paper cites GPT-4 Technical Report.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis GPT-4 Technical Report

Reference 1

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Observation 53b54936-415f-4b4b-87aa-d35edbb2f974 · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Hallucination of Multimodal Large Language Models: A Survey

Reference 2

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Observation 46ac92b6-66d1-4f32-a153-7bd9639daf00 · outbound

This paper cites Meteor: An automatic metric for mt evaluation with improved correlation with hu- man judgments.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Meteor: An automatic metric for mt evaluation with improved correlation with hu- man judgments

Reference 3

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Observation e18927b6-fc24-4f4d-b9a1-8047bd4be67e · outbound

This paper cites Let there be a clock on the beach: Reducing object halluci- nation in image captioning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Let there be a clock on the beach: Reducing object halluci- nation in image captioning

Reference 4

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Observation eb578b46-5dc6-434d-b65a-ce5d649f28ca · outbound

This paper cites Deconfounded visual question generation with causal infer- ence.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Deconfounded visual question generation with causal infer- ence

Reference 5

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Observation 43a5f770-eeb2-405c-ad5e-ff1fbd5e3bb1 · outbound

This paper cites Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Truth forest: Toward multi-scale truthfulness in large language models through intervention without tuning

Reference 6

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Observation 43a899a2-b14d-4f2a-8046-7f772f664d93 · outbound

This paper cites Can we edit multimodal large language models? In Proceed- ings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 13877–13888, Singapore, Dec.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Can we edit multimodal large language models? In Proceed- ings of the 2023 Conference on Empirical Methods in Nat- ural Language Processing, pages 13877–13888, Singapore, Dec

Reference 7

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Observation b962f58a-79de-4715-a4c2-abf8045d715d · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Instructblip: Towards general- purpose vision-language models with instruction tuning

Reference 8

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Observation 51d74d30-8b7a-49a5-b64d-5c453ae4a34d · outbound

This paper cites Detecting and preventing hallucinations in large vision language models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Detecting and preventing hallucinations in large vision language models

Reference 9

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Observation 6524c262-fd51-4af4-bd40-8a262f231092 · outbound

This paper cites OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

Reference 10

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Observation c451d919-fd57-4d5a-870f-bdd73dec706e · outbound

This paper cites Hallucination Augmented Contrastive Learning for Multimodal Large Language Model.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Hallucination Augmented Contrastive Learning for Multimodal Large Language Model

Reference 11

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This paper cites Sophia Koepke, Cordelia Schmid, and Zeynep Akata.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Sophia Koepke, Cordelia Schmid, and Zeynep Akata

Reference 12

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Observation b1320a8b-f8a5-458e-9aab-89de72c5e46e · outbound

This paper cites Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision

Reference 13

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Observation 0eb5e618-1401-4359-88c5-68f1dfa3653c · outbound

This paper cites Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

Reference 14

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Observation 6080b7f9-1903-4463-b7f8-7cd87364986b · outbound

This paper cites Inference-time intervention: Elic- iting truthful answers from a language model.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Inference-time intervention: Elic- iting truthful answers from a language model

Reference 15

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Observation b422cd40-c5ff-4f91-9c40-29dd52adb3ab · outbound

This paper cites Evaluating object hallucination in large vision-language models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Evaluating object hallucination in large vision-language models

Reference 16

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Observation f264525f-efda-4223-ab90-9c1db45e986a · outbound

This paper cites Microsoft coco: Common objects in context.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Microsoft coco: Common objects in context

Reference 17

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Observation a7b11f5a-90ee-4163-b9dc-78f7e026f707 · outbound

This paper cites Revisiting the role of language priors in vision-language models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Revisiting the role of language priors in vision-language models

Reference 18

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Observation 169a2792-0cc7-4000-80e7-efb62a26da1d · outbound

This paper cites Show, deconfound and tell: Im- age captioning with causal inference.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Show, deconfound and tell: Im- age captioning with causal inference

Reference 19

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Observation f0cceb10-3767-4269-8754-e21758536f63 · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 20

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Visual instruction tuning

Reference 21

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Observation d356f334-1839-42f6-bcd3-8c0f869dbb6e · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis A Survey on Hallucination in Large Vision-Language Models

Reference 22

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 23

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Observation b4a1dded-850a-4466-82ea-67c4e4ea3b03 · outbound

This paper cites Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models

Reference 24

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Observation dd083165-b5ac-4874-a6de-877b45601e0d · outbound

This paper cites Towards vision-language mechanistic interpretabil- ity: A causal tracing tool for blip.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Towards vision-language mechanistic interpretabil- ity: A causal tracing tool for blip

Reference 25

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This paper cites Direct and indirect effects.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Direct and indirect effects

Reference 26

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Causality

Reference 27

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This paper cites The seven tools of causal inference, with re- flections on machine learning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis The seven tools of causal inference, with re- flections on machine learning

Reference 28

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This paper cites Causal inference in statistics: A primer.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Causal inference in statistics: A primer

Reference 29

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This paper cites VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models

Reference 30

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Object hallucination in image cap- tioning

Reference 31

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This paper cites A causal framework to quantify the robustness of mathematical reasoning with lan- guage models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis A causal framework to quantify the robustness of mathematical reasoning with lan- guage models

Reference 32

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Aligning Large Multimodal Models with Factually Augmented RLHF

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Cider: Consensus-based image description evalua- tion

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This paper cites Cross modality bias in visual question answering: A causal view with possible worlds vqa.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Cross modality bias in visual question answering: A causal view with possible worlds vqa

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Vigc: Visual instruction generation and correction

Reference 36

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This paper cites AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 37

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This paper cites Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

Reference 38

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Show, attend and tell: Neural image caption gen- eration with visual attention

Reference 39

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This paper cites LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models

Reference 40

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Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Vigor: Improving visual ground- ing of large vision language models with fine-grained reward modeling

Reference 41

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This paper cites Decon- founded image captioning: A causal retrospect.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Decon- founded image captioning: A causal retrospect

Reference 42

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This paper cites Causal attention for vision-language tasks.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Causal attention for vision-language tasks

Reference 43

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This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

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This paper cites mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration

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This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 46

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This paper cites HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data

Reference 47

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Observation 75b2354e-78e5-47ae-9eb3-0503b26da340 · outbound

This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Inpaint Anything: Segment Anything Meets Image Inpainting

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This paper cites RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

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This paper cites Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment

Reference 50

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This paper cites Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Reference 51

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This paper cites Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Causal-debias: Unifying debiasing in pretrained language models and fine-tuning via causal invariant learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:01:33.085263Z

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This paper cites Analyzing and mitigating object hallucination in large vision-language models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Analyzing and mitigating object hallucination in large vision-language models

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:01:33.061248Z

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This paper cites Minigpt-4: Enhancing vision-language understanding with advanced large language models.

Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis Minigpt-4: Enhancing vision-language understanding with advanced large language models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T23:01:33.040316Z

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Pith citing papers

Observation 9da6fa52-8336-4240-96ab-babed217c04a · inbound

Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding cites this paper.

Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding Who Brings the Frisbee: Probing Hidden Hallucination Factors in Large Vision-Language Model via Causality Analysis

Reference 80

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