71 citations · 296 across the 26 of their papers we have counts for
5 papers · 1 filter
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics
Ximing Lu, Sean Welleck, Peter West +9
The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constra…
Generated Knowledge Prompting for Commonsense Reasoning
Jiacheng Liu, Alisa Liu, Ximing Lu +5
It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate…
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models
Peter West, Chandra Bhagavatula, Jack Hessel +6
The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In th…
Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics
Sean Welleck, Peter West, Jize Cao +1
Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance…
Surface Form Competition: Why the Highest Probability Answer Isn't Always Right
Ari Holtzman, Peter West, Vered Shwartz +2
Large language models have shown promising results in zero-shot settings (Brown et al.,2020; Radford et al., 2019). For example, they can perform multiple choice tasks simply by co…