1 citations · 1 across the 5 of their papers we have counts for
5 papers
Symbolic Representation for Any-to-Any Generative Tasks
Jiaqi Chen, Xiaoye Zhu, Yue Wang +9
We propose a symbolic generative task description language and a corresponding inference engine capable of representing arbitrary multimodal tasks as structured symbolic flows. Unl…
Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection
Jiaqi Chen, Xiaoye Zhu, Tianyang Liu +11
Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Although past methods have achieved good perform…
Explicit Inductive Inference using Large Language Models
Tianyang Liu, Tianyi Li, Liang Cheng +1
Large Language Models (LLMs) are reported to hold undesirable attestation bias on inference tasks: when asked to predict if a premise P entails a hypothesis H, instead of consideri…
Improving Bird's Eye View Semantic Segmentation by Task Decomposition
Tianhao Zhao, Yongcan Chen, Yu Wu +8
Semantic segmentation in bird's eye view (BEV) plays a crucial role in autonomous driving. Previous methods usually follow an end-to-end pipeline, directly predicting the BEV segme…
Rethinking Tabular Data Understanding with Large Language Models
Tianyang Liu, Fei Wang, Muhao Chen
Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area. In this…