33 citations · 63 across the 13 of their papers we have counts for
13 papers
On Meta-Prompting
Adrian de Wynter, Xun Wang, Qilong Gu +1
Modern large language models (LLMs) are capable of interpreting input strings as instructions, or prompts, and carry out tasks based on them. Unlike traditional learners, LLMs cann…
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…
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Mingzhen Sun, Weining Wang, Zihan Qin +3
Video generation necessitates both global coherence and local realism. This work presents a novel non-autoregressive method GLOBER, which first generates global features to obtain…
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…
In-context Autoencoder for Context Compression in a Large Language Model
Tao Ge, Jing Hu, Lei Wang +3
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly c…
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…
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
Zijia Zhao, Longteng Guo, Tongtian Yue +5
Building general-purpose models that can perceive diverse real-world modalities and solve various tasks is an appealing target in artificial intelligence. In this paper, we present…
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…
An Evaluation on Large Language Model Outputs: Discourse and Memorization
Adrian de Wynter, Xun Wang, Alex Sokolov +2
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-…
Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory
Guangyue Peng, Tao Ge, Wen Luo +2
Semiparametric language models (LMs) have shown promise in various Natural Language Processing (NLP) tasks. However, they utilize non-parametric memory as static storage, which lac…
Latent Prompt Tuning for Text Summarization
Yubo Zhang, Xingxing Zhang, Xun Wang +2
Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties…
Extensible Prompts for Language Models on Zero-shot Language Style Customization
Tao Ge, Jing Hu, Li Dong +5
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL). X-Prompt instructs an LLM with not only NL but also an extensible v…
EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation
Tao Ge, Si-Qing Chen, Furu Wei
We introduce EdgeFormer -- a parameter-efficient Transformer for on-device seq2seq generation under the strict computation and memory constraints. Compared with the previous parame…