Papers › HallE-Control: Controlling Object Hallucination in Large Multimodal Models

HallE-Control: Controlling Object Hallucination in Large Multimodal Models

3 Oct 2023arXiv:2310.01779archive 2025-07-28

Bohan Zhai, Shijia Yang, Chenfeng Xu, Sheng Shen, Kurt Keutzer, Chunyuan Li, Manling Li

Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that is, in performing detailed captioning. To address this, we introduce CCEval, a GPT-4 assisted evaluation method for detailed captioning. Interestingly, while LMMs demonstrate minimal object existence hallucination in existing VQA benchmarks, our proposed evaluation reveals continued susceptibility to such hallucinations. In this paper, we make the first attempt to investigate such hallucination from different aspects, including image resolution, the language decoder size, and instruction data amount, quality, granularity. Our findings underscore the unwarranted inference when the language description includes details at a finer object granularity than what the vision module can ground or verify, thus inducing hallucination. To control such hallucinations, we further attribute the reliability of captioning to contextual knowledge (involving only contextually grounded objects) and parametric knowledge (containing inferred objects by the model). Thus, we introduce HallE-Control, a controllable LMM in terms of Hallucination in object Existence. HallE-Control can condition the captioning to shift between (i) exclusively depicting contextual knowledge for grounded objects and (ii) blending it with parametric knowledge to imagine inferred objects. Our method reduces hallucination by 44% compared to LLaVA_(7B) and maintains the object coverage.

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bronyayang/HallE_Switch officialmentioned in papermentioned on GitHubpytorch report
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expand2square bronyayang/HallE_Switch/llava/mm_utils.py official repository ran · our draft was wrong no licence file found · pointer only · 592b3c1a88f93d7c · report
load_generated_captions bronyayang/halle_control/cceval.py official repository ran · our draft was wrong no licence file found · pointer only · 0adf9e0153200aed · report
load_image bronyayang/HallE_Switch/eval/model_controller.py official repository ran · honoured contract no licence file found · pointer only · 9b3c1cb391672ccb · report
load_image_from_base64 bronyayang/HallE_Switch/llava/mm_utils.py official repository ran no licence file found · pointer only · c3ee9d07c900dd55 · report
print_metrics bronyayang/halle_control/cceval.py official repository ran · our draft was wrong no licence file found · pointer only · 1e1fb53310bf974a · report
get_peft_state_maybe_zero_3 bronyayang/HallE_Switch/llava/train/train_switch.py official repository unverified no licence file found · pointer only · fa1225dfac92bc0d · report
get_peft_state_non_lora_maybe_zero_3 bronyayang/HallE_Switch/llava/train/train_switch.py official repository unverified no licence file found · pointer only · 1c53657305b66e9f · report
maybe_zero_3 bronyayang/HallE_Switch/llava/train/train_switch.py official repository unverified no licence file found · pointer only · 616ffbdc154ed2d8 · report
process_images bronyayang/HallE_Switch/llava/mm_utils.py official repository unverified no licence file found · pointer only · 344dff4791fd1381 · report

Tasks

AttributeDecoderHallucinationObjectObject HallucinationVisual Question Answering (VQA)

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Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutGPT-4Label SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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