ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations

• URL: http://arxiv.org/abs/2504.14429v1
• Date: Sun, 20 Apr 2025 00:10:44 GMT
• Title: ResNetVLLM-2: Addressing ResNetVLLM's Multi-Modal Hallucinations
• Authors: Ahmad Khalil, Mahmoud Khalil, Alioune Ngom,
• Abstract summary: Large Language Models (LLMs) have transformed natural language processing (NLP) tasks, but they suffer from hallucination, generating plausible yet factually incorrect content.<n>This issue extends to Video-Language Models (VideoLLMs), where textual descriptions may inaccurately represent visual content, resulting in multi-modal hallucinations.<n>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.
• Score: 0.40964539027092917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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