221 citations · 1.1k across the 24 of their papers we have counts for
6 papers · 1 filter
PAL: Program-aided Language Models
Luyu Gao, Aman Madaan, Shuyan Zhou +5
Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("f…
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…
Oversquashing in GNNs through the lens of information contraction and graph expansion
Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang +2
The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for pr…
DocPrompting: Generating Code by Retrieving the Docs
Shuyan Zhou, Uri Alon, Frank F. Xu +3
Publicly available source-code libraries are continuously growing and changing. This makes it impossible for models of code to keep current with all available APIs by simply traini…
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…
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Uri Alon, Frank F. Xu, Junxian He +3
Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore…