Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

We present QLoRA, an efficient finetuning approach that reduces memory usage enough to
finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit …

Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …

Abstract We introduce Inference-Time Intervention (ITI), a technique designed to enhance
the" truthfulness" of large language models (LLMs). ITI operates by shifting model activations …

Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks
(DNNs) training, and they usually train a DNN for each single visual recognition task …

With the prevalence of pre-trained language models (PLMs) and the pre-training–fine-tuning
paradigm, it has been continuously shown that larger models tend to yield better …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Expanding the language coverage of speech technology has the potential to improve
access to information for many more people. However, current speech technology is …