Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

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 …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

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 …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …

Large Language Models (LLMs) have revolutionized natural language processing tasks with
remarkable success. However, their formidable size and computational demands present …

Quantization is an indispensable technique for serving Large Language Models (LLMs) and
has recently found its way into LoRA fine-tuning. In this work we focus on the scenario where …

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

Understanding long real-world videos requires modeling of long-range visual
dependencies. To this end we explore video-first architectures building on the common …

We present Generalized LoRA (GLoRA), an advanced approach for universal parameter-
efficient fine-tuning tasks. Enhancing Low-Rank Adaptation (LoRA), GLoRA employs a …