21 citations · 63 across the 37 of their papers we have counts for
10 papers · 2 filters
Efficient Algorithms for Recognizing Weighted Tree-Adjoining Languages
Alexandra Butoi, Tim Vieira, Ryan Cotterell +1
The class of tree-adjoining languages can be characterized by various two-level formalisms, consisting of a context-free grammar (CFG) or pushdown automaton (PDA) controlling anoth…
Tokenization and the Noiseless Channel
Vilém Zouhar, Clara Meister, Juan Luis Gastaldi +3
Subword tokenization is a key part of many NLP pipelines. However, little is known about why some tokenizer and hyperparameter combinations lead to better downstream model performa…
A Formal Perspective on Byte-Pair Encoding
Vilém Zouhar, Clara Meister, Juan Luis Gastaldi +4
Byte-Pair Encoding (BPE) is a popular algorithm used for tokenizing data in NLP, despite being devised initially as a compression method. BPE appears to be a greedy algorithm at fa…
Structured Voronoi Sampling
Afra Amini, Li Du, Ryan Cotterell
Gradient-based sampling algorithms have demonstrated their effectiveness in text generation, especially in the context of controlled text generation. However, there exists a lack o…
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…
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective
Tianyu Liu, Afra Amini, Mrinmaya Sachan +1
Tasks that model the relation between pairs of tokens in a string are a vital part of understanding natural language. Such tasks, in general, require exhaustive pair-wise compariso…
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…