184 citations · 390 across the 4 of their papers we have counts for
12 papers
Language Models of Code are Few-Shot Commonsense Learners
Aman Madaan, Shuyan Zhou, Uri Alon +2
We address the general task of structured commonsense reasoning: given a natural language input, the goal is to generate a graph such as an event -- or a reasoning-graph. To employ…
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…
On the Bottleneck of Graph Neural Networks and its Practical Implications
Uri Alon, Eran Yahav
Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate i…
A Structural Model for Contextual Code Changes
Shaked Brody, Uri Alon, Eran Yahav
We address the problem of predicting edit completions based on a learned model that was trained on past edits. Given a code snippet that is partially edited, our goal is to predict…
Adversarial Examples for Models of Code
Noam Yefet, Uri Alon, Eran Yahav
Neural models of code have shown impressive results when performing tasks such as predicting method names and identifying certain kinds of bugs. We show that these models are vulne…
Structural Language Models of Code
Uri Alon, Roy Sadaka, Omer Levy +1
We address the problem of any-code completion - generating a missing piece of source code in a given program without any restriction on the vocabulary or structure. We introduce a…