42 citations · 72 across the 15 of their papers we have counts for
6 papers · 1 filter
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Yuxin Ren, Qipeng Guo, Zhijing Jin +4
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a…
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui +5
The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the r…
Efficient Prompting via Dynamic In-Context Learning
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ryan Cotterell +1
The primary way of building AI applications is shifting from training specialist models to prompting generalist models. A common practice for prompting generalist models, often ref…
Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus
Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma +3
Several recent papers claim human parity at sentence-level Machine Translation (MT), especially in high-resource languages. Thus, in response, the MT community has, in part, shifte…
Controlled Text Generation with Natural Language Instructions
Wangchunshu Zhou, Yuchen Eleanor Jiang, Ethan Wilcox +2
Large language models generate fluent texts and can follow natural language instructions to solve a wide range of tasks without task-specific training. Nevertheless, it is notoriou…
Neural Multi-Source Morphological Reinflection
Katharina Kann, Ryan Cotterell, Hinrich Schütze
We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pair…