26 citations · 55 across the 11 of their papers we have counts for
12 papers
Boosting Natural Language Generation from Instructions with Meta-Learning
Budhaditya Deb, Guoqing Zheng, Ahmed Hassan Awadallah
Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with impr…
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning
Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah +1
Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance ga…
Knowledge Infused Decoding
Ruibo Liu, Guoqing Zheng, Shashank Gupta +5
Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factuall…
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding
Subhabrata Mukherjee, Xiaodong Liu, Guoqing Zheng +6
Most recent progress in natural language understanding (NLU) has been driven, in part, by benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now matched…
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion
Budhaditya Deb, Guoqing Zheng, Milad Shokouhi +1
We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and se…
MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces
Srinagesh Sharma, Guoqing Zheng, Ahmed Hassan Awadallah
Albeit the universal representational power of pre-trained language models, adapting them onto a specific NLP task still requires a considerably large amount of labeled data. Effec…