260 citations · 755 across the 21 of their papers we have counts for
24 papers
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
Hao Yan, Saurabh Srivastava, Yintao Tai +3
Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the…
Accessing Higher Dimensions for Unsupervised Word Translation
Sida I. Wang
The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fai…
READ: Recurrent Adaptation of Large Transformers
John Nguyen, Sid Wang, Ke Li +1
Fine-tuning large-scale Transformers has led to the explosion of many AI applications across Natural Language Processing and Computer Vision tasks. However, fine-tuning all pre-tra…
TreePiece: Faster Semantic Parsing via Tree Tokenization
Sid Wang, Akshat Shrivastava, Sasha Livshits
Autoregressive (AR) encoder-decoder neural networks have proved successful in many NLP problems, including Semantic Parsing -- a task that translates natural language to machine-re…
Privately Customizing Prefinetuning to Better Match User Data in Federated Learning
Charlie Hou, Hongyuan Zhan, Akshat Shrivastava +4
In Federated Learning (FL), accessing private client data incurs communication and privacy costs. As a result, FL deployments commonly prefinetune pretrained foundation models on a…
LEVER: Learning to Verify Language-to-Code Generation with Execution
Ansong Ni, Srini Iyer, Dragomir Radev +4
The advent of large language models trained on code (code LLMs) has led to significant progress in language-to-code generation. State-of-the-art approaches in this area combine LLM…