Pengcheng Yin
Hi! I am Pengcheng, a research scientist at the learning for code team at Google Brain. I work on problems in the intersection of natural language processing and machine learning for software engineering. My long-term research goal is to build models to let developers communicate to computers in their own language. You can find more about my research at my personal website (http://pengcheng.in).
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SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL
Satya Gundabathula
Hanjun Dai
TMLR (2024)
Spider2.0-GUI: Can Multimodal Agents Achieve Expert Proficiency in Data Science and Engineering?
Ruisheng Cao
Fangyu Lei
Haoyuan Wu
Jixuan Chen
Yeqiao Fu
Hongcheng Gao
Xinzhuang Xiong
Hanchong Zhang
Yuchen Mao
Wenjing Hu
Tianbao Xie
Hongshen Xu
Danyang Zhang
Sida Wang
Caiming Xiong
Ansong Ni
Qian Liu
Victor Zhong
Lu Chen
Kai Yu
Tao Yu
2024
UQE: A Query Engine for Unstructured Databases
Hanjun Dai
Bethany Wang
Sherry Yang
Phitchaya Mangpo Phothilimthana
Advances in Neural Information Processing Systems (NeurIPS) (2024)
SQLPrompt: Improved In-context Learning for Few-shot Text-to-SQL
Hanjun Dai
Findings of Conference on Empirical Methods in Natural Language Processing (EMNLP) (2023)
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery
Sharan Narang
Jacob Devlin
Maarten Bosma
Hyung Won Chung
Sebastian Gehrmann
Parker Schuh
Sasha Tsvyashchenko
Abhishek Rao
Yi Tay
Noam Shazeer
Nan Du
Reiner Pope
James Bradbury
Guy Gur-Ari
Toju Duke
Henryk Michalewski
Xavier Garcia
Liam Fedus
David Luan
Barret Zoph
Ryan Sepassi
David Dohan
Shivani Agrawal
Mark Omernick
Marie Pellat
Aitor Lewkowycz
Erica Moreira
Rewon Child
Oleksandr Polozov
Zongwei Zhou
Brennan Saeta
Michele Catasta
Jason Wei
Kathy Meier-Hellstern
arxiv:2204.02311 (2022)
Compositional Generalization and Decomposition in Neural Program Synthesis
Joey Hong
Manzil Zaheer
Deep Learning for Code (DL4C) Workshop at ICLR'22 (2022)
