Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models GPT-4 Technical Report

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Qwen Technical Report

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Qwen2.5-VL Technical Report

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This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Grounding everything: Emerging localiza- tion properties in vision-language transformers

Reference 4

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This paper cites Evaluating a large language model on searching for gui layouts.Proceedings of the ACM on Human-Computer Interaction, 7(EICS):1–37, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Evaluating a large language model on searching for gui layouts.Proceedings of the ACM on Human-Computer Interaction, 7(EICS):1–37, 2023

Reference 5

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This paper cites Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020

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This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

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This paper cites In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation

Reference 8

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This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

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This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 10

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This paper cites A survey on multimodal large lan- guage models for autonomous driving.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models A survey on multimodal large lan- guage models for autonomous driving

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This paper cites SADL: An Effective In-Context Learning Method for Compositional Visual QA.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models SADL: An Effective In-Context Learning Method for Compositional Visual QA

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

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This paper cites Chatgpt outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences, 120(30):e2305016120, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Chatgpt outperforms crowd workers for text-annotation tasks.Proceedings of the National Academy of Sciences, 120(30):e2305016120, 2023

Reference 14

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Detecting and preventing hallucinations in large vision language models

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This paper cites A comprehensive survey of deep learn- ing for image captioning.ACM Computing Surveys (CsUR), 51(6):1–36, 2019.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models A comprehensive survey of deep learn- ing for image captioning.ACM Computing Surveys (CsUR), 51(6):1–36, 2019

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This paper cites Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Gqa: A new dataset for real-world visual reasoning and compositional question answering

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models

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This paper cites Survey of hallucination in natural language generation.ACM computing surveys, 55(12):1–38, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Survey of hallucination in natural language generation.ACM computing surveys, 55(12):1–38, 2023

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This paper cites Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

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This paper cites Contrastive Decoding: Open-ended Text Generation as Optimization.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Contrastive Decoding: Open-ended Text Generation as Optimization

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This paper cites Clip surgery for better explainability with enhancement in open- vocabulary tasks.arXiv e-prints, pages arXiv–2304, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Clip surgery for better explainability with enhancement in open- vocabulary tasks.arXiv e-prints, pages arXiv–2304, 2023

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This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Evaluating Object Hallucination in Large Vision-Language Models

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models A survey of multimodel large language models

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Microsoft coco: Common objects in context

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This paper cites Aligning large multi-modal model with robust instruction tuning.CoRR, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Aligning large multi-modal model with robust instruction tuning.CoRR, 2023

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Improved baselines with visual instruction tuning

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell

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This paper cites Learning transferable visual models from natural language supervi- sion.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Learning transferable visual models from natural language supervi- sion

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Object Hallucination in Image Captioning

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models A-okvqa: A benchmark for visual question answering using world knowl- edge

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Aligning Large Multimodal Models with Factually Augmented RLHF

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Stanford alpaca: An instruction-following llama model, 2023

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Sclip: Rethink- ing self-attention for dense vision-language inference

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This paper cites Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Surgical-LVLM: Learning to Adapt Large Vision-Language Model for Grounded Visual Question Answering in Robotic Surgery

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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This paper cites ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models

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Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Caption Anything: Interactive Image Description with Diverse Multimodal Controls

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This paper cites Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving.arXiv preprint arXiv:2312.09245, 2023.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Drivemlm: Aligning multi-modal large language models with behavioral planning states for au- tonomous driving.arXiv preprint arXiv:2312.09245, 2023

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This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

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This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models MiniCPM-V: A GPT-4V Level MLLM on Your Phone

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Observation 17801c05-cec1-42a6-aacc-867ccdf304dd · outbound

This paper cites Woodpecker: Hallucination correction for multimodal large language models.Science China Information Sciences, 67(12):220105, 2024.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Woodpecker: Hallucination correction for multimodal large language models.Science China Information Sciences, 67(12):220105, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:42.622977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 92015d10-50dc-43c7-a817-b1ebbaa634cb · outbound

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

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:40.942389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e1a60c6-4179-4a62-8d22-b860337d70a4 · outbound

This paper cites HuatuoGPT, towards Taming Language Model to Be a Doctor.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models HuatuoGPT, towards Taming Language Model to Be a Doctor

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:41.054437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d29c0b0d-3e93-47a7-9713-1f3b465572b9 · outbound

This paper cites Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:41.235492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60c69dda-7610-4e4f-99bf-92abdeafcb71 · outbound

This paper cites Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:41.382368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c42e6af-2ab2-49b8-ae91-db9e666073d1 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:41.573398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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