arXiv:2501.01926v3 Announce Type: replace-cross Abstract: Large vision-language models (LVLMs) have shown remarkable capabilities in visual-language understanding. Despite their success, LVLMs still suffer from generating hallucinations in complex generation tasks, leading to inconsistencies between visual inputs and generated content. To address this issue, some approaches have introduced inference-time interventions, such as contrastive decoding, to reduce overreliance on language priors. However, these approaches overlook hallucinations stemming from position bias and spurious inter-modalit
The proliferation of advanced LVLMs has amplified the challenge of hallucinations, making mitigation a critical and active area of research to improve reliability and trust in AI systems.
Reliable and accurate AI outputs are fundamental for effective deployment across various industries; mitigating hallucinations in LVLMs directly addresses a key barrier to broader AI adoption and trust.
This research contributes to making LVLMs more dependable by reducing their propensity to generate factually incorrect or inconsistent content, enhancing their utility in high-stakes applications.
- · AI developers
- · Enterprise AI adopters
- · Generative AI users
- · Providers of unreliable generative AI
LVLMs become more trustworthy for content generation and understanding tasks.
Increased adoption of LVLMs in sectors requiring high accuracy, like healthcare and legal.
Reduced need for extensive human oversight in fact-checking AI-generated content, accelerating workflow automation.
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Read at arXiv cs.AI