We present a reality check on large language models and inspect the prom...
Large and sparse feed-forward networks (S-FFN) such as Mixture-of-Expert...
The advent of large language models trained on code (code LLMs) has led ...
Recent work has shown that fine-tuning large pre-trained language models...
Scaling up language models has led to unprecedented performance gains, b...
Multilingual pre-trained models are known to suffer from the curse of
mu...
Large language models, which are often trained for hundreds of thousands...
People write personalized greeting cards on various occasions. While pri...
Mixture of Experts layers (MoEs) enable efficient scaling of language mo...
Large-scale autoregressive language models such as GPT-3 are few-shot
le...
Synthesizing data for semantic parsing has gained increasing attention
r...
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power ...
We present BRIDGE, a powerful sequential architecture for modeling
depen...
Translating natural language utterances to executable queries is a helpf...
We present GraPPa, an effective pre-training approach for table semantic...
Natural language interfaces to databases (NLIDB) democratize end user ac...
We introduce DART, a large dataset for open-domain structured data recor...
Word embeddings derived from human-generated corpora inherit strong gend...
We present CoSQL, a corpus for building cross-domain, general-purpose
da...
We focus on the cross-domain context-dependent text-to-SQL generation ta...
We present SParC, a dataset for cross-domainSemanticParsing inContext th...
Multi-hop reasoning is an effective approach for query answering (QA) ov...
We present new data and semantic parsing methods for the problem of mapp...