40 citations · 133 across the 11 of their papers we have counts for
16 papers · 1 filter
Token Alignment via Character Matching for Subword Completion
Ben Athiwaratkun, Shiqi Wang, Mingyue Shang +7
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens. This struggle stems from tokenization, where partial to…
Code Representation Learning At Scale
Dejiao Zhang, Wasi Ahmad, Ming Tan +5
Recent studies have shown that code language models at scale demonstrate significant performance gains on downstream tasks, i.e., code generation. However, most of the existing wor…
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding
Jun Wang, Patrick Ng, Alexander Hanbo Li +5
Most recent research on Text-to-SQL semantic parsing relies on either parser itself or simple heuristic based approach to understand natural language query (NLQ). When synthesizing…
Efficient Few-Shot Fine-Tuning for Opinion Summarization
Arthur Bražinskas, Ramesh Nallapati, Mohit Bansal +1
Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opi…
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Zheng Li, Zijian Wang, Ming Tan +5
Large-scale pre-trained sequence-to-sequence models like BART and T5 achieve state-of-the-art performance on many generative NLP tasks. However, such models pose a great challenge…
Improving Factual Consistency of Abstractive Summarization via Question Answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu +7
A commonly observed problem with the state-of-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. The f…