A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Transformers have revolutionized almost all natural language processing (NLP) tasks but
suffer from memory and computational complexity that scales quadratically with sequence …

Foundation model development attracts a rapidly expanding body of contributors, scientists,
and applications. To help shape responsible development practices, we introduce the …

This work introduces ATTEMPT (ATTEntional Mixture of Prompt Tuning), a new modular,
multi-task, and parameterefficient language model (LM) tuning approach that combines …

Few-shot learning is valuable in many real-world applications, but learning a generalizable
model without overfitting to the few labeled datapoints is challenging. In this work, we focus …

The recent successes and spread of large neural language models (LMs) call for a thorough
understanding of their computational ability. Describing their computational abilities through …

We present a generalizable classification approach that leverages Large Language Models
(LLMs) to facilitate the detection of implicitly encoded social meaning in conversations. We …

Recent work in NLP has shown promising results in training models on large amounts of
tasks to achieve better generalization. However, it is not well-understood how tasks are …

Existing research studies on cross-sentence relation extraction in long-form multi-party
conversations aim to improve relation extraction without considering the explainability of …

For a viewpoint-diverse news recommender, identifying whether two news articles express
the same viewpoint is essential. One way to determine" same or different" viewpoint is …