19 citations · 29 across the 11 of their papers we have counts for
6 papers · 1 filter
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Weiting Tan, Haoran Xu, Lingfeng Shen +5
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…
Do pretrained Transformers Learn In-Context by Gradient Descent?
Lingfeng Shen, Aayush Mishra, Daniel Khashabi
The emergence of In-Context Learning (ICL) in LLMs remains a remarkable phenomenon that is partially understood. To explain ICL, recent studies have created theoretical connections…
The Trickle-down Impact of Reward (In-)consistency on RLHF
Lingfeng Shen, Sihao Chen, Linfeng Song +5
Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for…
A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement
Lingfeng Shen, Haiyun Jiang, Lemao Liu +1
Generating proper embedding of sentences through an unsupervised way is beneficial to semantic matching and retrieval problems in real-world scenarios. This paper presents Represen…
Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency
Lingfeng Shen, Weiting Tan, Boyuan Zheng +1
With growing capabilities of large language models, prompting them has become the dominant way to access them. This has motivated the development of strategies for automatically se…
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification
Lingfeng Shen, Ze Zhang, Haiyun Jiang +1
Adversarial attack serves as a major challenge for neural network models in NLP, which precludes the model's deployment in safety-critical applications. A recent line of work, dete…