Transformer-based large language models (LLMs) have achieved great success with the
growing model size. LLMs' size grows by 240× every two years, which outpaces the …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Quantization of deep neural networks (DNN) has been proven effective for compressing and
accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the …

Open domain question answering (ODQA) is a longstanding task aimed at answering factual
questions from a large knowledge corpus without any explicit evidence in natural language …

Transformer architecture has become the de-facto model for many machine learning tasks
from natural language processing and computer vision. As such, improving its computational …

Quantization is a technique to reduce the computation and memory cost of DNN models,
which are getting increasingly large. Existing quantization solutions use fixed-point integer …

We present the first comprehensive empirical evaluation of pre-trained language models
(PLMs) for legal natural language processing (NLP) in order to examine their effectiveness …

New chat AI applications like ChatGPT offer an advanced understanding of question context
and memory across multi-step tasks, such that experiments can test its deductive reasoning …

Existing machine reading comprehension methods use a fixed stride to chunk long texts,
which leads to missing contextual information at the boundaries of the chunks and a lack of …

In emerging DNN serving systems, queries are usually batched to fully leverage hardware
resources, and all the queries in a batch run through the complete model and return at the …