151 citations · 183 across the 5 of their papers we have counts for
12 papers
A Systematic Evaluation of Large Language Models of Code
Frank F. Xu, Uri Alon, Graham Neubig +1
Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. However, the current state-o…
Learning Structural Edits via Incremental Tree Transformations
Ziyu Yao, Frank F. Xu, Pengcheng Yin +2
While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed mode…
In-IDE Code Generation from Natural Language: Promise and Challenges
Frank F. Xu, Bogdan Vasilescu, Graham Neubig
A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of p…
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Frank F. Xu, Lei Ji, Botian Shi +4
Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirec…
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
Frank F. Xu, Zhengbao Jiang, Pengcheng Yin +2
Open-domain code generation aims to generate code in a general-purpose programming language (such as Python) from natural language (NL) intents. Motivated by the intuition that dev…
How Can We Know What Language Models Know?
Zhengbao Jiang, Frank F. Xu, Jun Araki +1
Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profe…