INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling | Paper Citation Record

Observation cde163f2-8c77-43a1-b3cf-0994f4aace27 · outbound

This paper cites Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

Reference 1

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Observation a1fb25db-3cec-43ef-906f-8fc625f13c61 · outbound

This paper cites Qwen Technical Report.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Qwen Technical Report

Reference 2

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Observation 32146d21-d3c0-49b7-836c-928f63a46e6b · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 3

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Observation abd1f3f1-8060-4670-9419-2bded2f1845b · outbound

This paper cites Audio chord recognition with recurrent neural networks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Audio chord recognition with recurrent neural networks

Reference 4

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Observation a5f89206-c5b7-4a57-ae89-6a8068f7b646 · outbound

This paper cites Explaining a series of models by propagating shapley values.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Explaining a series of models by propagating shapley values

Reference 5

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Observation 14a3c217-acfc-41a1-9175-0c4b255ad614 · outbound

This paper cites HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation

Reference 6

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Observation 4eec48b3-1663-4ede-a27a-99988b8e0564 · outbound

This paper cites Are We on the Right Way for Evaluating Large Vision-Language Models?.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Are We on the Right Way for Evaluating Large Vision-Language Models?

Reference 7

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Observation 77c2f478-91d0-4563-8c17-358dd4ebcf98 · outbound

This paper cites Mitigating Hallucination in Visual Language Models with Visual Supervision.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Mitigating Hallucination in Visual Language Models with Visual Supervision

Reference 8

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Observation b6e23cfd-04b9-40f6-b682-be1301f0a10e · outbound

This paper cites How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites

Reference 9

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Observation 5adf8b47-d680-4fd8-9a0f-c9cb8648e60d · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 10

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Observation bb014128-057b-4da0-accb-0fa9e84d5f85 · outbound

This paper cites HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding

Reference 11

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Observation 27dec42c-0982-4fe4-8aea-80063af9da73 · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality

Reference 12

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Observation 9b05a7d3-e268-4b27-9fba-4d7c5a8afa63 · outbound

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

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 13

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Observation c9c02951-86d5-4b28-b4c6-f89920d25ef2 · outbound

This paper cites Discovering and Explaining the Representation Bottleneck of DNNs.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Discovering and Explaining the Representation Bottleneck of DNNs

Reference 14

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Observation 4a22e055-b58d-4069-afb2-7158c7c8c8b2 · outbound

This paper cites Explaining deepfake detection by analysing im- age matching.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Explaining deepfake detection by analysing im- age matching

Reference 15

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Observation bba5652d-6683-4270-b8d7-0c0cee8a4b2a · outbound

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

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

Reference 16

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Observation de86c1ac-25fa-4518-8f01-552187d3beb7 · outbound

This paper cites Multi-modal hal- lucination control by visual information grounding.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Multi-modal hal- lucination control by visual information grounding

Reference 17

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Observation d0b5b00c-a6cc-499a-95e4-7b5538186ba4 · outbound

This paper cites Shapley val- ues for feature selection: The good, the bad, and the axioms.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Shapley val- ues for feature selection: The good, the bad, and the axioms

Reference 18

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Observation 922a97d6-8cd5-40e9-a4f3-86892197d85c · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 19

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Observation 77f4446a-42ab-4603-9988-178a70cc16fe · outbound

This paper cites An axiomatic approach to the concept of interaction among players in cooperative games.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling An axiomatic approach to the concept of interaction among players in cooperative games

Reference 20

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Observation ec0d1c72-e24f-42c8-bdae-6eaf36412b51 · outbound

This paper cites Sequence Transduction with Recurrent Neural Networks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Sequence Transduction with Recurrent Neural Networks

Reference 21

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Observation 187b7394-8d7e-4911-a615-871cd4906a1c · outbound

This paper cites A simplified bar- gaining model for the n-person cooperative game.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling A simplified bar- gaining model for the n-person cooperative game

Reference 22

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Observation fb62258b-cb84-4f16-b3d2-7e649616f458 · outbound

This paper cites The curious case of neural text degeneration.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The curious case of neural text degeneration

Reference 23

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Observation 3be5026c-c421-4e8c-b802-c24f8f2571ad · outbound

This paper cites spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing

Reference 24

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Observation d2482827-9a6d-4da5-bc0c-2d06dcbf784d · outbound

This paper cites Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 25

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Observation c0a21d6c-fcd0-4947-9f66-03d72cb4917a · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:24.493981Z

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Observation 85fe5ffc-b528-4464-a4d3-eac8446b038e · outbound

This paper cites Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models

Reference 27

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Observation 7d197cd5-7f59-4a43-988a-e9eb4e90aee4 · outbound

This paper cites GPT-4o System Card.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling GPT-4o System Card

Reference 28

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Observation ae821b85-f618-48d4-84dd-01223ad74e82 · outbound

This paper cites Vcoder: Ver- satile vision encoders for multimodal large language models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Vcoder: Ver- satile vision encoders for multimodal large language models

Reference 29

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Observation 9e1fb101-0060-4cb0-a6ea-95f24be9a26a · outbound

This paper cites Hallucination augmented contrastive learn- ing for multimodal large language model.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Hallucination augmented contrastive learn- ing for multimodal large language model

Reference 30

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Observation 650fe551-ea2f-416f-8532-9aa34c0f1079 · outbound

This paper cites mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Reference 31

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Observation 8b304bf2-f282-4b24-8413-fbb69e49c2f0 · outbound

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INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Contrastive Decoding: Open-ended Text Generation as Optimization

Reference 32

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INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Evaluating Object Hallucination in Large Vision-Language Models

Reference 34

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INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Microsoft coco: Common objects in context

Reference 35

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Observation 4e80fea9-ad4e-464c-9660-ffea4130c2d0 · outbound

This paper cites Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

Reference 36

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Observation aeaffdb4-21ca-48e3-aef6-f3b0309677d1 · outbound

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INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Llava-bench in the wild dataset

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:23.638230Z

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Observation b0b1efb2-7dcc-450b-a7b4-f8a51466909d · outbound

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INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Improved baselines with visual instruction tuning, 2023

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:23.365231Z

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Observation 81672b28-54e1-412e-8f76-d4d9f27341a0 · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling MMBench: Is Your Multi-modal Model an All-around Player?

Reference 39

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Observation 6d962263-043a-4890-9fac-f8d2c0a7c1b6 · outbound

This paper cites ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models

Reference 40

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Observation 9e4ebc1a-37c7-4fcb-8372-16f4320294f6 · outbound

This paper cites ALOHa: A New Measure for Hallucination in Captioning Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling ALOHa: A New Measure for Hallucination in Captioning Models

Reference 41

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Observation ec5bf09a-d69e-431f-aa31-6fb443969130 · outbound

This paper cites Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning

Reference 42

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Observation 29eeb48a-e5e8-4c2c-a46b-9dfcece3af37 · outbound

This paper cites A Unified Game-Theoretic Interpretation of Adversarial Robustness.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling A Unified Game-Theoretic Interpretation of Adversarial Robustness

Reference 43

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Observation f7b76607-d9c0-49f4-a0e9-d0350ac9c7c2 · outbound

This paper cites Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?

Reference 44

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Observation f529a361-e618-4838-bf63-674bd14f7232 · outbound

This paper cites Defining and quantifying the emergence of sparse concepts in dnns.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Defining and quantifying the emergence of sparse concepts in dnns

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:23.153811Z

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Observation c7583dc6-9f33-4fa0-b27a-04ca6a6e529a · outbound

This paper cites Object hallucination in image cap- tioning.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Object hallucination in image cap- tioning

Reference 46

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Observation bf777500-2db8-4457-8708-d6c7a1881bef · outbound

This paper cites A-okvqa: A benchmark for visual question answering using world knowl- edge.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling A-okvqa: A benchmark for visual question answering using world knowl- edge

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:22.623496Z

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Observation 134f230f-6abd-48f1-bc93-ccac0a28ee95 · outbound

This paper cites A value for n-person games.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling A value for n-person games

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:22.352688Z

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Observation 4b4ff39a-1a9f-4ecc-9496-2c30b67cc78b · outbound

This paper cites The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism

Reference 49

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Observation cc52176b-9033-4924-9e8f-ff9bf702d255 · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 50

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This paper cites The many shapley values for model explanation.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The many shapley values for model explanation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:22.121952Z

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Observation 483ef97e-44a0-4c56-a920-789bcbdf22de · outbound

This paper cites The shapley taylor interaction index.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The shapley taylor interaction index

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:21.928008Z

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Observation f7bbec4e-1e31-4cd5-bee2-e9a28e5dae72 · outbound

This paper cites Sequence to sequence learning with neural networks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Sequence to sequence learning with neural networks

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:21.648284Z

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Observation ef6b4625-eaa3-4d29-93ec-b0a1065b24c1 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.520881Z

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Observation c19d4d64-6667-4f9a-991a-0ced4f2edaf2 · outbound

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

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.525397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a4c0d5b-4252-49cc-9412-e49e25669e1c · outbound

This paper cites Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Diagnosing the Compositional Knowledge of Vision Language Models from a Game-Theoretic View

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:38:19.809275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 318c25dc-19f0-48ea-b975-02a146fb879c · outbound

This paper cites A unified approach to interpret- ing and boosting adversarial transferability.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling A unified approach to interpret- ing and boosting adversarial transferability

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:21.453645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca4a43e2-58b5-4920-8419-6ad8d5c82730 · outbound

This paper cites Interpreting attributions and interactions of adversar- ial attacks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Interpreting attributions and interactions of adversar- ial attacks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:21.177952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 77c0045a-de0e-4004-8161-4ae7263fe224 · outbound

This paper cites RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling RITUAL: Random Image Transformations as a Universal Anti-hallucination Lever in Large Vision Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.543126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 291c248f-be04-408f-ad8b-2f4e79a1109e · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.547606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2012884b-c9c0-4439-b567-4e795a5af1ff · outbound

This paper cites Towards understanding the generalization of deepfake detectors from a game-theoretical view.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Towards understanding the generalization of deepfake detectors from a game-theoretical view

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.921201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d2164764-afa4-44f7-93a9-57ab4100f894 · outbound

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

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.557416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1249e4de-e633-47bc-a3ec-c0384cf3736e · outbound

This paper cites mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.772787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 050f154d-3fe5-4eaf-b8f7-2c0f1e30488e · outbound

This paper cites Modeling context in referring expres- sions.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Modeling context in referring expres- sions

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.724467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0161e570-e149-4c75-8a73-c60890974455 · outbound

This paper cites Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.689823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 96b9cab1-d2a4-41a3-aa61-3ccb702988e5 · outbound

This paper cites Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.574901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 945083b6-f25e-4346-8cda-b30ad4879084 · outbound

This paper cites Halle-switch: Controlling ob- ject hallucination in large vision language models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Halle-switch: Controlling ob- ject hallucination in large vision language models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.669545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0ae7641-632e-49fc-a02b-7846e89581c5 · outbound

This paper cites Building interpretable interaction trees for deep nlp models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Building interpretable interaction trees for deep nlp models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.653107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 97bc30db-ad68-4f09-9ef3-52995a9da03f · outbound

This paper cites Technical Note: Game-Theoretic Interactions of Different Orders.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Technical Note: Game-Theoretic Interactions of Different Orders

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:38:19.724425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ea42423c-a0f1-4822-a4c0-89f4b104e7b0 · outbound

This paper cites Interpreting multivariate shapley interac- tions in dnns.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Interpreting multivariate shapley interac- tions in dnns

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.637752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 624be49e-5c6c-4b77-91a8-fce28c0273bd · outbound

This paper cites Explaining gen- eralization power of a dnn using interactive concepts.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Explaining gen- eralization power of a dnn using interactive concepts

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.622141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e02966d-90f4-4502-aaf9-660001192938 · outbound

This paper cites Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.601377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f633e1e-5a50-4c4b-bc46-ee9819a304bc · outbound

This paper cites The Polling-based Object Probing Evaluation (POPE) [33] utilizes images sampled from several datasets, including MSCOCO [35], A-OKVQA [47], and GQA [26].

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The Polling-based Object Probing Evaluation (POPE) [33] utilizes images sampled from several datasets, including MSCOCO [35], A-OKVQA [47], and GQA [26]

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.607116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c0db2e3d-7c47-4cc1-a523-b4e07dd744a7 · outbound

This paper cites As shown in Tab.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling As shown in Tab

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.592658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a5f312c9-410c-4083-aed1-bceda856df19 · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:38:20.579002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2428d4a3-430c-417d-9f43-2122bc72bdff · outbound

This paper cites Through experiments on CHAIR [46] and MME [19] benchmarks, we analyze how the interaction guidance co- efficient k affects the performance of INTER.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Through experiments on CHAIR [46] and MME [19] benchmarks, we analyze how the interaction guidance co- efficient k affects the performance of INTER

Reference 79

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:38:20.565237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b98024fe-4daa-4488-939a-18b2d5b3dd89 · outbound

This paper cites The results, as shown in the Tab.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The results, as shown in the Tab

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.549878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddc901fa-c9c4-4dd7-ba52-e189e078691b · outbound

This paper cites As shown in Tab.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling As shown in Tab

Reference 81

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:38:20.534946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation defe6cd6-866c-4951-8aa3-a9cd43e97b6e · outbound

This paper cites 10, 19 and 20.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling 10, 19 and 20

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.519750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af3be004-b5ae-4e4c-a261-b1d82dcf65b4 · outbound

This paper cites 12, 23 and 24.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling 12, 23 and 24

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.504515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8de149a9-a5a9-414d-a7db-9902c042dea8 · outbound

This paper cites 8 to 13, 15, 16 and 19 to 26, we demonstrated the effectiveness of INTER in correcting the Greedy Search across various benchmarks.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling 8 to 13, 15, 16 and 19 to 26, we demonstrated the effectiveness of INTER in correcting the Greedy Search across various benchmarks

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.488666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 89c1f77e-73c8-4eb8-bcf2-9fbf0235e02b · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:38:20.472206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37e6d830-d325-497c-b8e6-b96d45aab416 · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:38:20.456978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4b41057f-1334-46b1-aaad-5288789c308d · outbound

This paper cites We conducted experiments with M3ID [17], Ritual [59] and SID [27] in Tab.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling We conducted experiments with M3ID [17], Ritual [59] and SID [27] in Tab

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.441900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92dbc7f1-a87a-49c6-844f-70792efc36be · outbound

This paper cites We conducted experiments with DeepSeek-VL2 [60] on the visual grounding task.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling We conducted experiments with DeepSeek-VL2 [60] on the visual grounding task

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:38:20.427414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f7ea1276-1890-405f-8bde-6650a8abbb3f · outbound

This paper cites The value range of I(A)yt could be influenced by several factors, e.g., benchmarks, LVLMs, etc.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling The value range of I(A)yt could be influenced by several factors, e.g., benchmarks, LVLMs, etc

Reference 89

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:38:20.412188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9d487f5e-a3a1-4ef4-b53f-64c24dd8b809 · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 2018

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:38:22.871959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7d3728dc-8d71-4c22-b751-2ea4a757c123 · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.332718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4feeb5fa-42da-4d45-8334-fdff83141b03 · outbound

This paper cites an unresolved cited work.

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling Unresolved cited work

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T19:38:19.300877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers