14 citations · 54 across the 10 of their papers we have counts for
10 papers
Batch Prompting: Efficient Inference with Large Language Model APIs
Zhoujun Cheng, Jungo Kasai, Tao Yu
Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch pr…
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems
Fan Zhou, Haoyu Dong, Qian Liu +3
Numerical reasoning over natural language has been a long-standing goal for the research community. However, cutting-edge language models have proven difficult to reliably generali…
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Fan Zhou, Mengkang Hu, Haoyu Dong +3
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstr…
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Haoyu Dong, Zhoujun Cheng, Xinyi He +6
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been prop…
Understanding Pixel-level 2D Image Semantics with 3D Keypoint Knowledge Engine
Yang You, Chengkun Li, Yujing Lou +5
Pixel-level 2D object semantic understanding is an important topic in computer vision and could help machine deeply understand objects (e.g. functionality and affordance) in our da…
FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining
Zhoujun Cheng, Haoyu Dong, Ran Jia +4
Tables store rich numerical data, but numerical reasoning over tables is still a challenge. In this paper, we find that the spreadsheet formula, which performs calculations on nume…
HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
Zhoujun Cheng, Haoyu Dong, Zhiruo Wang +6
Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing…
Semantic Correspondence via 2D-3D-2D Cycle
Yang You, Chengkun Li, Yujing Lou +4
Visual semantic correspondence is an important topic in computer vision and could help machine understand objects in our daily life. However, most previous methods directly train o…
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Yang You, Yujing Lou, Chengkun Li +5
Detecting 3D objects keypoints is of great interest to the areas of both graphics and computer vision. There have been several 2D and 3D keypoint datasets aiming to address this pr…
Human Correspondence Consensus for 3D Object Semantic Understanding
Yujing Lou, Yang You, Chengkun Li +5
Semantic understanding of 3D objects is crucial in many applications such as object manipulation. However, it is hard to give a universal definition of point-level semantics that e…