457 citations · 1k across the 30 of their papers we have counts for
35 papers
Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Fan Bai, Hengshuo Miao, Sanjit S Batra +3
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-leve…
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies
Shiyue Zhang, Shijie Wu, Ozan Irsoy +4
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-…
BloombergGPT: A Large Language Model for Finance
Shijie Wu, Ozan Irsoy, Steven Lu +6
The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Larg…
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
David Mueller, Nicholas Andrews, Mark Dredze
Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often re…
Zero-shot Cross-lingual Transfer is Under-specified Optimization
Shijie Wu, Benjamin Van Durme, Mark Dredze
Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language. We post…
What Makes Data-to-Text Generation Hard for Pretrained Language Models?
Moniba Keymanesh, Adrian Benton, Mark Dredze
Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Prev…