88 citations · 147 across the 15 of their papers we have counts for
15 papers
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models
Zhihan Zhang, Shuohang Wang, Wenhao Yu +6
Large language models (LLMs) can perform a wide range of tasks by following natural language instructions, without the necessity of task-specific fine-tuning. Unfortunately, the pe…
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
Pluggable Neural Machine Translation Models via Memory-augmented Adapters
Yuzhuang Xu, Shuo Wang, Peng Li +4
Although neural machine translation (NMT) models perform well in the general domain, it remains rather challenging to control their generation behavior to satisfy the requirement o…
Sparse Modular Activation for Efficient Sequence Modeling
Liliang Ren, Yang Liu, Shuohang Wang +3
Recent hybrid models combining Linear State Space Models (SSMs) with self-attention mechanisms have demonstrated impressive results across a range of sequence modeling tasks. Howev…
PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents
Simeng Sun, Yang Liu, Shuohang Wang +2
Strategies such as chain-of-thought prompting improve the performance of large language models (LLMs) on complex reasoning tasks by decomposing input examples into intermediate ste…
LMGQS: A Large-scale Dataset for Query-focused Summarization
Ruochen Xu, Song Wang, Yang Liu +5
Query-focused summarization (QFS) aims to extract or generate a summary of an input document that directly answers or is relevant to a given query. The lack of large-scale datasets…
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
Yichong Xu, Ruochen Xu, Dan Iter +4
While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utiliza…
Small Models are Valuable Plug-ins for Large Language Models
Canwen Xu, Yichong Xu, Shuohang Wang +3
Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned wit…
In-Context Demonstration Selection with Cross Entropy Difference
Dan Iter, Reid Pryzant, Ruochen Xu +4
Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. However, selecting the best in-context examples is challenging because mod…
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
Yang Liu, Dan Iter, Yichong Xu +3
The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have bee…
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Zhuosheng Zhang, Shuohang Wang, Yichong Xu +6
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language mod…
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Shuohang Wang, Yichong Xu, Yuwei Fang +5
Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge. However, the indexing and retrieving of large-scale corpora bring considera…
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
Zhixing Tan, Xiangwen Zhang, Shuo Wang +1
Prompting has recently been shown as a promising approach for applying pre-trained language models to perform downstream tasks. We present Multi-Stage Prompting (MSP), a simple and…
Language Models are Good Translators
Shuo Wang, Zhaopeng Tu, Zhixing Tan +3
Recent years have witnessed the rapid advance in neural machine translation (NMT), the core of which lies in the encoder-decoder architecture. Inspired by the recent progress of la…
Neural Machine Translation: A Review of Methods, Resources, and Tools
Zhixing Tan, Shuo Wang, Zonghan Yang +4
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machi…