7 citations · 9 across the 4 of their papers we have counts for
6 papers · 1 filter
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation
Tu Vu, Mohit Iyyer, Xuezhi Wang +8
Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detail…
Dialect-robust Evaluation of Generated Text
Jiao Sun, Thibault Sellam, Elizabeth Clark +6
Evaluation metrics that are not robust to dialect variation make it impossible to tell how well systems perform for many groups of users, and can even penalize systems for producin…
Exploring and Predicting Transferability across NLP Tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai +5
Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other…
Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification
Tu Vu, Mohit Iyyer
While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque. In this paper, we inv…
Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment
Tu Vu, Vered Shwartz
Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words. Spec…
Sentence Simplification with Memory-Augmented Neural Networks
Tu Vu, Baotian Hu, Tsendsuren Munkhdalai +1
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstr…