Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models

Abstract: Recent advancements in large vision-LLMs (LVLMs) have demonstrated impressive capability in visual information understanding with human language. Despite these advances, LVLMs still face challenges with multimodal hallucination, such as generating text descriptions of objects that are not present in the visual information. However, the underlying fundamental reasons of multimodal hallucinations remain poorly explored. In this paper, we propose a new perspective, suggesting that the inherent biases in LVLMs might be a key factor in hallucinations. Specifically, we systematically identify a semantic shift bias related to paragraph breaks (\n\n), where the content before and after '\n\n' in the training data frequently exhibit significant semantic changes. This pattern leads the model to infer that the contents following '\n\n' should be obviously different from the preceding contents with less hallucinatory descriptions, thereby increasing the probability of hallucinatory descriptions subsequent to the '\n\n'. We have validated this hypothesis on multiple publicly available LVLMs. Besides, we find that deliberately inserting '\n\n' at the generated description can induce more hallucinations. A simple method is proposed to effectively mitigate the hallucination of LVLMs by skipping the output of '\n'.

• The paper demonstrates that removing paragraph breaks from outputs effectively reduces hallucinations in large vision-language models.
• The method employs MiHI and MiHO techniques to lower hallucination rates across various models such as BakLLaVA, InstructBLIP-7B, and MiniGPT-v2.
• This lightweight approach offers a practical solution for enhancing the reliability of vision-language systems in high-stakes applications.

Reducing Hallucination in Large Vision-LLMs with Skip \n Methodology

The paper "Skip \textbackslash n: A Simple Method to Reduce Hallucination in Large Vision-LLMs" investigates multimodal hallucination in large vision-LLMs (LVLMs) and proposes a novel approach to mitigate such hallucinations. Hallucinations in LVLMs refer to instances where models generate descriptions of objects that are not present in the visual input. This issue is particularly acute in applications requiring high precision, such as autonomous driving and medical diagnosis.

Fundamental Problem Investigated

The authors identify a specific semantic shift bias related to paragraph breaks ('\textbackslash n\textbackslash n') as a previously unexamined source of hallucination. They observe that models often learn from training data patterns where content before and after paragraph breaks differ semantically, leading models to expect significant topic shifts after such breaks. This understanding results in increased hallucination probability after these shifts, as LVLMs anticipate fundamentally different subsequent content.

Proposed Solution: The Skip \n Method

The authors introduce a straightforward method that reduces hallucination by preventing the generation of paragraph breaks during output processing. The solution is twofold: altering prompts on the input side (Mitigating Hallucinations during Input - MiHI) and modifying logits on the output side to circumvent '\textbackslash n\textbackslash n' generation (Mitigating Hallucinations during Output - MiHO). The combined application of these techniques achieves effective hallucination reduction without the need for extensive retraining or additional data.

Key Findings and Experimental Results

The study evaluates the hypothesis and the proposed method across various LVLMs, including BakLLaVA, InstructBLIP-7B, and MiniGPT-v2, among others. Quantitative analysis shows significant hallucination reduction when applying the Skip \n method:

• The paper demonstrates that content generated after paragraph breaks contained higher rates of hallucinations across different models.
• The inclusion of paragraph breaks was shown to significantly raise hallucination probability.
• The Skip \n method, in both MiHI and MiHO forms, substantially lowered hallucination rates, particularly with greedy decoding strategies which tend to be more conservative in generation.

Implications and Future Directions

This research solidifies the significance of model bias understanding in reducing errors in vision-language alignment. The approach provides a lightweight solution compared to more complex retraining-based methods, showing promise for immediate practical deployment in various applications necessitating high accuracy.

The paper also opens new avenues for exploration regarding the behavior of model biases and hallucinations, particularly how such biases might evolve or diminish as model scales continue to increase. These findings could potentially shift future research towards more bias-aware training regimes and decoding strategies.

In conclusion, this work adds a new dimension to understanding hallucinations in LVLMs through the seemingly modest yet impactful Skip \n methodology. By addressing the semantic shift bias related to paragraph breaks, the paper contributes significantly to safer and more reliable AI deployment in critical arenas.