298 citations · 338 across the 10 of their papers we have counts for
11 papers
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
Xiao Liu, Hanyu Lai, Hao Yu +6
We present WebGLM, a web-enhanced question-answering system based on the General Language Model (GLM). Its goal is to augment a pre-trained large language model (LLM) with web sear…
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation
Ji Qi, Jifan Yu, Teng Tu +12
Despite the recent emergence of video captioning models, how to generate vivid, fine-grained video descriptions based on the background knowledge (i.e., long and informative commen…
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Yifan Xu, Nicklas Hansen, Zirui Wang +3
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so…
GLM-130B: An Open Bilingual Pre-trained Model
Aohan Zeng, Xiao Liu, Zhengxiao Du +15
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as good…
Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models
Tyler A. Chang, Yifan Xu, Weijian Xu +1
In this paper, we detail the relationship between convolutions and self-attention in natural language tasks. We show that relative position embeddings in self-attention layers are…
Pose Recognition with Cascade Transformers
Ke Li, Shijie Wang, Xiang Zhang +3
In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them in…
Co-Scale Conv-Attentional Image Transformers
Weijian Xu, Yifan Xu, Tyler Chang +1
In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, t…
Line Segment Detection Using Transformers without Edges
Yifan Xu, Weijian Xu, David Cheung +1
In this paper, we present a joint end-to-end line segment detection algorithm using Transformers that is post-processing and heuristics-guided intermediate processing (edge/junctio…
Guided Variational Autoencoder for Disentanglement Learning
Zheng Ding, Yifan Xu, Weijian Xu +4
We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement lear…
Neural Program Synthesis By Self-Learning
Yifan Xu, Lu Dai, Udaikaran Singh +2
Neural inductive program synthesis is a task generating instructions that can produce desired outputs from given inputs. In this paper, we focus on the generation of a chunk of ass…
Rethinking Exposure Bias In Language Modeling
Yifan Xu, Kening Zhang, Haoyu Dong +3
Exposure bias describes the phenomenon that a language model trained under the teacher forcing schema may perform poorly at the inference stage when its predictions are conditioned…