71 citations · 110 across the 9 of their papers we have counts for
9 papers
Arko-T: A Foundation Model for Text-to-Structured 3D Generation
Liang Wang, Zhaoyang Xi, Zekai Xiang +5
Text-to-3D systems can now synthesize a model from a single sentence, yet the result is a shape to render, not a design to edit. We present Arko-T, a 4B-parameter text-to-design mo…
Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
Liang Wang, Heng Meng, Zekai Xiang +4
Text-to-CAD generation aims to create parametric CAD models from natural language, enabling rapid prototyping and intuitive design workflows. However, existing benchmarks focus on…
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…
MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion
Zi Gong, Yinpeng Guo, Pingyi Zhou +3
Code completion is a valuable topic in both academia and industry. Recently, large-scale mono-programming-lingual (MonoPL) pre-training models have been proposed to boost the perfo…
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta +19
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solut…
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training
Xin Wang, Yasheng Wang, Yao Wan +5
Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various w…
Compilable Neural Code Generation with Compiler Feedback
Xin Wang, Yasheng Wang, Yao Wan +7
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated softwa…
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation
Yihe Wang, Yitong Li, Yasheng Wang +6
Real human conversation data are complicated, heterogeneous, and noisy, from which building open-domain dialogue systems remains a challenging task. In fact, such dialogue data sti…
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
Xin Wang, Yasheng Wang, Fei Mi +7
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code inte…