The paper proposes a four-stage self-verification prompting method but explicitly labels its experimental results as fabricated, so it cannot support its claimed gains.
ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations
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abstract
Large Language Models (LLMs) have transformed natural language processing (NLP) tasks, but they suffer from hallucination, generating plausible yet factually incorrect content. This issue extends to Video-Language Models (VideoLLMs), where textual descriptions may inaccurately represent visual content, resulting in multi-modal hallucinations. In this paper, we address hallucination in ResNetVLLM, a video-language model combining ResNet visual encoders with LLMs. We introduce a two-step protocol: (1) a faithfulness detection strategy that uses a modified Lynx model to assess semantic alignment between generated captions and ground-truth video references, and (2) a hallucination mitigation strategy using Retrieval-Augmented Generation (RAG) with an ad-hoc knowledge base dynamically constructed during inference. Our enhanced model, ResNetVLLM-2, reduces multi-modal hallucinations by cross-verifying generated content against external knowledge, improving factual consistency. Evaluation on the ActivityNet-QA benchmark demonstrates a substantial accuracy increase from 54.8% to 65.3%, highlighting the effectiveness of our hallucination detection and mitigation strategies in enhancing video-language model reliability.
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Enhancing Factual Accuracy and Citation Generation in LLMs via Multi-Stage Self-Verification
The paper proposes a four-stage self-verification prompting method but explicitly labels its experimental results as fabricated, so it cannot support its claimed gains.