Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines
incorrect details about an image in visual question answering. Existing studies
find VH instances only in existing image datasets, which results in biased
understanding of MLLMs' performance under VH due to limited diversity of such
VH instances. In this work, we propose a tool called VHTest to generate a
diverse set of VH instances. Specifically, VHTest finds some initial VH
instances in existing image datasets (e.g., COCO), generates a text description
for each VH mode, and uses a text-to-image generative model (e.g., DALL-E-3) to
generate VH images based on the text descriptions. We collect a benchmark
dataset with 1,200 VH instances in 8 VH modes using VHTest. We find that
existing MLLMs such as GPT-4V, LLaVA-1.5, and MiniGPT-v2 hallucinate for a
large fraction of the instances in our benchmark. Moreover, we find that
fine-tuning an MLLM using our benchmark dataset reduces its likelihood to
hallucinate without sacrificing its performance on other benchmarks. Our
benchmarks are publicly available: this https URL

通过使用一个工具称为 VHTest，我们生成了一个包含 8 种视觉幻觉模式的多样化 VH 实例数据集，并发现现有的多模态 LLM 如 GPT-4V，LLaVA-1.5 和 MiniGPT-v2 在我们的数据集中的大部分实例中出现幻觉，而使用我们的数据集对 MLLM 进行 fine-tuning 可以减少其幻觉可能性而不降低其在其他基准测试中的性能。

多模态大型语言模型的视觉幻觉

Visual Hallucinations of Multi-modal Large Language Models

Human communication is a complex and diverse process that not only involves
multiple factors such as language, commonsense, and cultural backgrounds but
also requires the participation of multimodal information, such as speech.
Large Language Model (LLM)-based multi-agent systems have demonstrated
promising performance in simulating human society. Can we leverage LLM-based
multi-agent systems to simulate human communication? However, current LLM-based
multi-agent systems mainly rely on text as the primary medium. In this paper,
we propose SpeechAgents, a multi-modal LLM based multi-agent system designed
for simulating human communication. SpeechAgents utilizes multi-modal LLM as
the control center for individual agent and employes multi-modal signals as the
medium for exchanged messages among agents. Additionally, we propose
Multi-Agent Tuning to enhance the multi-agent capabilities of LLM without
compromising general abilities. To strengthen and evaluate the effectiveness of
human communication simulation, we build the Human-Communication Simulation
Benchmark. Experimental results demonstrate that SpeechAgents can simulate
human communication dialogues with consistent content, authentic rhythm, and
rich emotions and demonstrate excellent scalability even with up to 25 agents,
which can apply to tasks such as drama creation and audio novels generation.
Code and models will be open-sourced at this https URL
com/0nutation/SpeechAgents

提出了一种基于多模态 LLM 的多智能体系统 SpeechAgents，用于模拟人类交流，并通过多智能体调优增强了 LLM 的多智能体能力，实验结果表明 SpeechAgents 可以具有一致的内容、真实的节奏和丰富的情感，同时在高达 25 个代理人的情况下表现出优秀的可扩展性。