1 code implementation • 22 Apr 2024 • Adrian de Wynter, Ishaan Watts, Nektar Ege Altıntoprak, Tua Wongsangaroonsri, Minghui Zhang, Noura Farra, Lena Baur, Samantha Claudet, Pavel Gajdusek, Can Gören, Qilong Gu, Anna Kaminska, Tomasz Kaminski, Ruby Kuo, Akiko Kyuba, Jongho Lee, Kartik Mathur, Petter Merok, Ivana Milovanović, Nani Paananen, Vesa-Matti Paananen, Anna Pavlenko, Bruno Pereira Vidal, Luciano Strika, Yueh Tsao, Davide Turcato, Oleksandr Vakhno, Judit Velcsov, Anna Vickers, Stéphanie Visser, Herdyan Widarmanto, Andrey Zaikin, Si-Qing Chen
Large language models (LLMs) and small language models (SLMs) are being adopted at remarkable speed, although their safety still remains a serious concern.
no code implementations • 20 Feb 2024 • Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam, Furu Wei
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs).
2 code implementations • 11 Dec 2023 • Adrian de Wynter, Xun Wang, Qilong Gu, Si-Qing Chen
We call these approaches meta-prompting, or prompting to obtain prompts.
1 code implementation • 29 Sep 2023 • Xin Cheng, Xun Wang, Tao Ge, Si-Qing Chen, Furu Wei, Dongyan Zhao, Rui Yan
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 unified translation engine.
1 code implementation • 15 Aug 2023 • 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.
1 code implementation • 13 Jul 2023 • Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, Furu Wei
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language models (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes.
1 code implementation • 5 Jun 2023 • Yujia Xie, Xun Wang, Si-Qing Chen, Wayne Xiong, Pengcheng He
Summarizing lengthy documents is a common and essential task in our daily lives.
no code implementations • 20 Apr 2023 • Minghui Zhang, Alex Sokolov, Weixin Cai, Si-Qing Chen
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).
no code implementations • 17 Apr 2023 • Adrian de Wynter, Xun Wang, Alex Sokolov, Qilong Gu, Si-Qing Chen
We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs).
no code implementations • 2 Mar 2023 • Guangyue Peng, Tao Ge, Si-Qing Chen, Furu Wei, Houfeng Wang
We demonstrate that SeMem improves the scalability of semiparametric LMs for continual learning over streaming data in two ways: (1) data-wise scalability: as the model becomes stronger through continual learning, it will encounter fewer difficult cases that need to be memorized, causing the growth of the non-parametric memory to slow down over time rather than growing at a linear rate with the size of training data; (2) model-wise scalability: SeMem allows a larger model to memorize fewer samples than its smaller counterpart because it is rarer for a larger model to encounter incomprehensible cases, resulting in a non-parametric memory that does not scale linearly with model size.
no code implementations • 20 Dec 2022 • Xun Wang, Tao Ge, Allen Mao, Yuki Li, Furu Wei, Si-Qing Chen
We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task.
no code implementations • 8 Dec 2022 • Xingxing Zhang, Yiran Liu, Xun Wang, Pengcheng He, Yang Yu, Si-Qing Chen, Wayne Xiong, Furu Wei
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 Transformers.
Ranked #2 on Text Summarization on SAMSum
no code implementations • NeurIPS 2023 • Tao Ge, Jing Hu, Li Dong, Shaoguang Mao, Yan Xia, Xun Wang, Si-Qing Chen, Furu Wei
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL).
no code implementations • 3 Nov 2022 • Yubo Zhang, Xingxing Zhang, Xun Wang, Si-Qing Chen, Furu Wei
In this paper, we propose Lotus (shorthand for Latent Prompt Tuning for Summarization), which is a single model that can be applied in both controlled and uncontrolled (without control signals) modes.
2 code implementations • 20 May 2022 • Tao Ge, Heming Xia, Xin Sun, Si-Qing Chen, Furu Wei
We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding.
Abstractive Text Summarization Grammatical Error Correction +4
2 code implementations • 30 Mar 2022 • Heming Xia, Tao Ge, Peiyi Wang, Si-Qing Chen, Furu Wei, Zhifang Sui
We propose Speculative Decoding (SpecDec), for the first time ever, to formally study exploiting the idea of speculative execution to accelerate autoregressive (AR) decoding.
1 code implementation • 16 Feb 2022 • 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.