Accumulative Decoding: An Innovative Method to Reduce Hallucinations in Vision-Language Models Without Training

Accumulative Decoding is an open-source project focused on solving the hallucination problem in large Vision-Language Models (VLMs). This project proposes a training-free accumulative decoding strategy that improves the accuracy and reliability of model outputs by enhancing the decoding process, lowers deployment barriers, and is applicable to various VLM architectures.

In recent years, VLMs have performed well in multimodal tasks, but the hallucination problem (generated content inconsistent with images) limits their application in critical fields such as healthcare and autonomous driving. Traditional solutions require extensive fine-tuning or annotation, while Accumulative Decoding uses a training-free decoding strategy. The causes of hallucinations include: biased training data, overly strong language priors, limitations of traditional decoding, and insufficient multimodal alignment.

Accumulative decoding workflow: 1. Multi-path sampling to explore multiple generation paths; 2. Confidence accumulation to evaluate current and historical information; 3. Detecting confidence anomalies to identify hallucination risks; 4. Dynamically recalibrating token probabilities. Advantages: No training cost, plug-and-play for easy integration, strong interpretability, and general applicability to various VLM architectures.

1. Image caption generation: Improve description accuracy; 2. Visual question answering: Suppress inconsistent answers and enhance reliability; 3. Multimodal content moderation: Reduce misjudgments due to inconsistent text and images; 4. Medical image analysis: Provide more reliable auxiliary diagnostic information.

Evaluation metrics include hallucination detection accuracy, description accuracy, semantic consistency, and inference latency. Experiments show: The hallucination rate on the MSCOCO dataset is significantly reduced, the answer accuracy in VQA tasks is improved, and the additional computational overhead is acceptable.

Current limitations: Increased computational and memory overhead, parameter sensitivity requiring tuning, and reduced effectiveness in complex scenarios. Future directions: Combining lightweight fine-tuning, adaptive parameter adjustment, extending to other modalities, and optimizing efficiency for real-time applications.

Environment requirements: Windows/Linux/macOS, 8GB+ memory, Python 3.8+. Installation steps: Clone the repository → Install dependencies → Configure the model → Run examples. Parameter tuning: Adjust the accumulative window size (balance dependency and overhead), confidence threshold (control hallucination detection sensitivity), and sampling temperature (lower to improve certainty).