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Large Language Models (LLMs) often exhibit judgment and decision-making patterns that resemble those of humans, posing them as attractive candidates for studying human cognition. They not only emulate rational norms such as risk and loss aversion, but also showcase human-like errors and biases, particularly in probability judgments and arithmetic operations. Despite their potential prospects, challenges…

Multimodal machine learning combines various data types such as text, images, and audio to create more accurate and comprehensive models. However, large multimodal models (LMMs), like LLaVA, have been facing problems dealing with high-resolution graphics due to their inflexible and inefficient nature. Many have recognized the necessity for methods that may adjust the number of…

Retrieval-augmented generation (RAG) has been used to enhance the capabilities of large language models (LLMs) by incorporating external knowledge. However, RAG is susceptible to retrieval corruption, a type of attack in which disruptive information is inserted into the document collection, leading to the generation of incorrect or misleading responses. This poses a serious threat to…