Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:19:45.907689Z
Paper Citation Record · LEDGER
As of 12 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2411.13697.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T16:19:45.907689Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T17:18:39.999709Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-10T17:18:42.348103Z
54 of 54 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation e856053a-6e0c-4a4f-b7fe-56e089518534 · outbound
Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Llama 3.1 8b instruct
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Neural module networks
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Rank analysis of incomplete block designs: I
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Modularized zero-shot VQA with pre-trained models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Breaking common sense: Whoops! A vision-and- language benchmark of synthetic and compositional images
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Efficient Multimodal Learning from Data-centric Perspective
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hudson and Christopher D
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hallucination augmented contrastive learning for multimodal large language model
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs What’s ”up” with vision-language models? investigating their strug- gle with spatial reasoning
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Shamma, Michael S
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Silkie: Preference Distillation for Large Visual Language Models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Evaluating object hallucination in large vision-language models
Reference 22
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Improved Baselines with Visual Instruction Tuning
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Visual instruction tuning
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs OK-VQA: A visual question answering benchmark requiring external knowledge
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Simple Open-Vocabulary Object Detection with Vision Transformers
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Compositional chain-of-thought prompting for large multimodal models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi
Reference 30
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs GPT-4 Technical Report
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Unresolved cited work
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Manning, Stefano Ermon, and Chelsea Finn
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Object hallucination in image cap- tioning
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Toolformer: Lan- guage models can teach themselves to use tools
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Averitec: A dataset for real-world claim verification with ev- idence from the web
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Towards VQA models that can read
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Reclip: A strong zero-shot baseline for referring expression compre- hension
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Aligning large multimodal models with factually aug- mented RLHF
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Winoground: Probing vision and language models for visio- linguistic compositionality
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Woodpecker: Hallucination Correction for Multimodal Large Language Models
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs RLAIF-V: aligning mllms through open-source AI feedback for super GPT-4V trustworthiness
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Less is more: Miti- gating multimodal hallucination from an EOS decision per- spective
Reference 49
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs ROME: evaluating pre-trained vision-language models on reasoning beyond visual common sense
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
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Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs Is the {sub} {relation} {obj}?
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InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model Decompose and Leverage Preferences from Expert Models for Improving Trustworthiness of MLLMs
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