BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models | Paper Citation Record

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This paper cites Gpt-4 technical report.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Gpt-4 technical report

Reference 1

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This paper cites Hierarchi- cal neural story generation.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Hierarchi- cal neural story generation

Reference 2

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This paper cites The claude 3 model family: Opus, sonnet, haiku.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models The claude 3 model family: Opus, sonnet, haiku

Reference 3

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This paper cites Qwen2.5-vl technical report.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Qwen2.5-vl technical report

Reference 4

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This paper cites Lan- guage models are few-shot learners.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Lan- guage models are few-shot learners

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This paper cites Gonzalez, Ion Stoica, and Eric P.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Gonzalez, Ion Stoica, and Eric P

Reference 6

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models InstructBLIP: Towards general-purpose vision-language models with instruction tuning

Reference 7

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This paper cites Density estimation using real NVP.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Density estimation using real NVP

Reference 8

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This paper cites Vec2face: Unveil hu- man faces from their blackbox features in face recognition.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Vec2face: Unveil hu- man faces from their blackbox features in face recognition

Reference 9

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BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Beam search strate- gies for neural machine translation

Reference 10

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This paper cites Made: Masked autoencoder for distribution es- timation.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Made: Masked autoencoder for distribution es- timation

Reference 11

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This paper cites The Llama 3 Herd of Models.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models The Llama 3 Herd of Models

Reference 12

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This paper cites Sequence Transduction with Recurrent Neural Networks.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Sequence Transduction with Recurrent Neural Networks

Reference 13

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Detecting and preventing hallucinations in large vision language models

Reference 14

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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This paper cites The curious case of neural text degeneration.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models The curious case of neural text degeneration

Reference 16

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 17

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This paper cites Self-introspective de- coding: Alleviating hallucinations for large vision-language models.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Self-introspective de- coding: Alleviating hallucinations for large vision-language models

Reference 18

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This paper cites Interpreting and editing vision-language representations to mitigate hallucinations.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Interpreting and editing vision-language representations to mitigate hallucinations

Reference 19

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This paper cites Glow: Generative flow with invertible 1x1 convolutions.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Glow: Generative flow with invertible 1x1 convolutions

Reference 20

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This paper cites Improved variational in- ference with inverse autoregressive flow.Advances in neural information processing systems, 2016.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Improved variational in- ference with inverse autoregressive flow.Advances in neural information processing systems, 2016

Reference 21

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 22

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This paper cites Multimodal foundation models: From specialists to general-purpose as- sistants.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Multimodal foundation models: From specialists to general-purpose as- sistants

Reference 23

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 24

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This paper cites Contrastive decoding: Open-ended text genera- tion as optimization.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Contrastive decoding: Open-ended text genera- tion as optimization

Reference 25

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Evaluating object hallucination in large vision-language models

Reference 26

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This paper cites Microsoft coco: Common objects in context.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Microsoft coco: Common objects in context

Reference 27

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This paper cites Visual instruction tuning.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Visual instruction tuning

Reference 28

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This paper cites Improved baselines with visual instruction tuning.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Improved baselines with visual instruction tuning

Reference 29

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

Reference 30

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This paper cites Paying more at- tention to image: A training-free method for alleviating hal- lucination in lvlms.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Paying more at- tention to image: A training-free method for alleviating hal- lucination in lvlms

Reference 31

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This paper cites interpreting gpt: the logit lens.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models interpreting gpt: the logit lens

Reference 32

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This paper cites Masked autoregressive flow for density estimation.Advances in neural information processing systems, 30, 2017.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Masked autoregressive flow for density estimation.Advances in neural information processing systems, 30, 2017

Reference 33

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Learning transferable visual models from natural language supervi- sion

Reference 34

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BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Variational inference with normalizing flows

Reference 35

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This paper cites Object hallucination in image cap- tioning.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Object hallucination in image cap- tioning

Reference 36

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This paper cites A comprehensive sur- vey of hallucination in large language, image, video and au- dio foundation models.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models A comprehensive sur- vey of hallucination in large language, image, video and au- dio foundation models

Reference 37

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This paper cites Trusting your evidence: Hallucinate less with context-aware decoding.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Trusting your evidence: Hallucinate less with context-aware decoding

Reference 38

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This paper cites Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding

Reference 39

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This paper cites Sequence to sequence learning with neural networks.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Sequence to sequence learning with neural networks

Reference 40

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This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 41

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This paper cites Gemma: Open Models Based on Gemini Research and Technology.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 42

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This paper cites Gemma 2: Improving open language models at a practical size.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Gemma 2: Improving open language models at a practical size

Reference 43

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 44

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 45

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This paper cites Bimal: Bijective maximum likelihood approach to domain adaptation in se- mantic scene segmentation.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Bimal: Bijective maximum likelihood approach to domain adaptation in se- mantic scene segmentation

Reference 46

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This paper cites Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution

Reference 47

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This paper cites Mitigating hallucinations in large vision-language models with instruction contrastive decoding.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Mitigating hallucinations in large vision-language models with instruction contrastive decoding

Reference 48

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This paper cites Qwen2.5 Technical Report.

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models Qwen2.5 Technical Report

Reference 49

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

BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models MiniGPT-4: Enhancing vision-language understanding with advanced large language models

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:23:04.123266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers