# Octopus: Alleviating Hallucination via Dynamic Contrastive Decoding

arXiv: https://arxiv.org/abs/2503.00361 · HTML: https://mlsanity.com/paper/2503.00361/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 50 of its 66 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://mlsanity.com/paper/2405.14734/) (2024)
- [Aligning Large Multimodal Models with Factually Augmented RLHF](https://mlsanity.com/paper/2309.14525/) (2023)
- [A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://mlsanity.com/paper/2206.01718/) (2022)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback](https://mlsanity.com/paper/2312.00849/) (2023)
- [AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation](https://mlsanity.com/paper/2311.07397/) (2023)
- [Detecting and Preventing Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2308.06394/) (2023)
- [Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization](https://mlsanity.com/paper/2311.16839/) (2023)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [Multi-Modal Hallucination Control by Visual Information Grounding](https://mlsanity.com/paper/2403.14003/) (2024)
- [Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding](https://mlsanity.com/paper/2403.18715/) (2024)
- [Aligning Modalities in Vision Large Language Models via Preference Fine-tuning](https://mlsanity.com/paper/2402.11411/) (2024)

## Built on by (most cited first, 24 of 24)

- [Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration](https://mlsanity.com/paper/2505.21472/) (2025)
- [Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers](https://mlsanity.com/paper/2507.23362/) (2025)
- [Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2512.01922/) (2025)
- [HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning](https://mlsanity.com/paper/2511.20515/) (2025)
- [SAVE: Sparse Autoencoder-Driven Visual Information Enhancement for Mitigating Object Hallucination](https://mlsanity.com/paper/2512.07730/) (2025)
- [Empowering Multimodal LLMs with External Tools: A Comprehensive Survey](https://mlsanity.com/paper/2508.10955/) (2025)
- [MACD: Model-Aware Contrastive Decoding via Counterfactual Data](https://mlsanity.com/paper/2602.01740/) (2026)
- [BIMA: Bijective Maximum Likelihood Learning Approach to Hallucination Prediction and Mitigation in Large Vision-Language Models](https://mlsanity.com/paper/2505.24649/) (2025)
- [Mitigating Cross-Image Information Leakage in Multi-Image Understanding with Large Vision-Language Models](https://mlsanity.com/paper/2508.13744/) (2025)
- [Context-Aware Decoding for Faithful Vision-Language Generation](https://mlsanity.com/paper/2601.05939/) (2026)
- [Scaling Test-Time Robustness of Vision-Language Models via Self-Critical Inference Framework](https://mlsanity.com/paper/2603.07659/) (2026)
- [BUZZY: Contrastive Scoring to Mitigate Text-Induced Bias in Multimodal Multiple-Choice QA](https://mlsanity.com/paper/2603.28026/) (2026)
- [Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning](https://mlsanity.com/paper/2604.25809/) (2026)
- [Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models](https://mlsanity.com/paper/2605.12258/) (2026)
- [Watermarking for Factuality: Guiding Vision-Language Models Toward Truth via Tri-layer Contrastive Decoding](https://mlsanity.com/paper/2510.14304/) (2025)
- [HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning](https://mlsanity.com/paper/2603.15253/) (2026)
- [Hallucination-aware intermediate representation edit in large vision-language models](https://mlsanity.com/paper/2603.29405/) (2026)
- [HTDC: Hesitation-Triggered Differential Calibration for Mitigating Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2604.12115/) (2026)
- [Online Self-Calibration Against Hallucination in Vision-Language Models](https://mlsanity.com/paper/2605.00323/) (2026)
- [Uncertainty-Aware Exploratory Direct Preference Optimization for Multimodal Large Language Models](https://mlsanity.com/paper/2605.04874/) (2026)
- [Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models](https://mlsanity.com/paper/2605.18160/) (2026)
- [Zero-MELO: Test-Time Evidence Calibration with Multimodal LLMs for Zero-Shot Micro-Gesture Recognition](https://mlsanity.com/paper/2608.14854/) (2026)
- [Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery](https://mlsanity.com/paper/2609.37263/) (2026)
- [Mitigating Video Hallucinations via Attention-Guided Representation Perturbation](https://mlsanity.com/paper/2610.19728/) (2026)

## Abstract (arXiv)

Large Vision-Language Models (LVLMs) have obtained impressive performance in visual content understanding and multi-modal reasoning. Unfortunately, these large models suffer from serious hallucination problems and tend to generate fabricated responses. Recently, several Contrastive Decoding (CD) strategies have been proposed to alleviate hallucination by introducing disturbed inputs. Although great progress has been made, these CD strategies mostly apply a one-size-fits-all approach for all input conditions. In this paper, we revisit this process through extensive experiments. Related results show that hallucination causes are hybrid and each generative step faces a unique hallucination challenge. Leveraging these meaningful insights, we introduce a simple yet effective Octopus-like framework that enables the model to adaptively identify hallucination types and create a dynamic CD workflow. Our Octopus framework not only outperforms existing methods across four benchmarks but also demonstrates excellent deployability and expansibility. Code is available at https://github.com/LijunZhang01/Octopus.