With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

The emergence of generative artificial intelligence (AI) and large language models (LLMs)
has marked a new era of natural language processing (NLP), introducing unprecedented …

Large language models (LLMs) have become increasingly capable, but their development
often requires substantial computational resources. Although model merging has emerged …

The landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

In this paper, we unveil that Language Models (LMs) can acquire new capabilities by
assimilating parameters from homologous models without retraining or GPUs. We first …

Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-
trained models directly in weight space: By adding the fine-tuned weights of different tasks …

Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …

This paper introduces an efficient fine-tuning method for large pre-trained models, offering
strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from …

We introduce SOLAR 10.7 B, a large language model (LLM) with 10.7 billion parameters,
demonstrating superior performance in various natural language processing (NLP) tasks …

Typical deep visual recognition models are capable of performing the one task they were
trained on. In this paper, we tackle the extremely difficult problem of combining completely …