The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage
numerous existing high-performing models instead of building machine learning models …

The ever-growing ecosystem of LLMs has posed a challenge in selecting the most
appropriate pre-trained model to fine-tune amidst a sea of options. Given constrained …

This paper investigates an under-explored but important problem: given a collection of pre-
trained neural networks, predicting their performance on each multi-modal task without fine …

This paper investigates an under-explored but important problem: given a collection of pre-
trained neural networks, predicting their performance on each multi-modal task without fine …

The rapid development of Multimodal Large Language Models (MLLMs) like GPT-4V has
marked a significant step towards artificial general intelligence. Existing methods mainly …

Selecting pretrained models for image classification often involves computationally intensive
finetuning. This study addresses a research gap in the standardized evaluation of …

The transformer architecture has shown significant improvements in the performance of
various natural language processing (NLP) tasks. One of the great advantages of …

Multimodal large language models (MLLMs), initiated with a trained LLM, first align images
with text and then fine-tune on multimodal mixed inputs. However, the MLLM …

The rapid advancements in Large Language Models (LLMs) have significantly expanded
their applications, ranging from multilingual support to domain-specific tasks and multimodal …

Characterizing users and items through vector representations is crucial for various tasks in
recommender systems. Recent approaches attempt to apply Large Language Models …