most citedMomentum Calibration for Text Generation

13 citations · 22 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20231 cited

SCALE: Synergized Collaboration of Asymmetric Language Translation Engines

Xin Cheng, Xun Wang, Tao Ge +4

In this paper, we introduce SCALE, a collaborative framework that connects compact Specialized Translation Models (STMs) and general-purpose Large Language Models (LLMs) as one uni…

cs.CL2023

A User-Centered Evaluation of Spanish Text Simplification

Adrian de Wynter, Anthony Hevia, Si-Qing Chen

We present an evaluation of text simplification (TS) in Spanish for a production system, by means of two corpora focused in both complex-sentence and complex-word identification. W…

cs.CL20232 cited

Interactive Editing for Text Summarization

Yujia Xie, Xun Wang, Si-Qing Chen +2

Summarizing lengthy documents is a common and essential task in our daily lives. Although recent advancements in neural summarization models can assist in crafting general-purpose…

cs.CL2023

Joint Repetition Suppression and Content Moderation of Large Language Models

Minghui Zhang, Alex Sokolov, Weixin Cai +1

Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs). As the ke…

cs.CL202213 cited

Momentum Calibration for Text Generation

Xingxing Zhang, Yiran Liu, Xun Wang +5

The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as…

cs.CL20226 cited

Lossless Acceleration for Seq2seq Generation with Aggressive Decoding

Tao Ge, Heming Xia, Xin Sun +2

We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speedi…