93 citations · 133 across the 13 of their papers we have counts for
14 papers
Knowledge Distillation with Training Wheels
Guanlin Liu, Anand Ramachandran, Tanmay Gangwani +2
Knowledge distillation is used, in generative language modeling, to train a smaller student model using the help of a larger teacher model, resulting in improved capabilities for t…
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long +4
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. Howe…
Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
Chris Richardson, Yao Zhang, Kellen Gillespie +5
Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language processing (NLP) systems. With the emergence of…
Learning to Retrieve Engaging Follow-Up Queries
Christopher Richardson, Sudipta Kar, Anjishnu Kumar +4
Open domain conversational agents can answer a broad range of targeted queries. However, the sequential nature of interaction with these systems makes knowledge exploration a lengt…
Improving Device Directedness Classification of Utterances with Semantic Lexical Features
Kellen Gillespie, Ioannis C. Konstantakopoulos, Xingzhi Guo +2
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature "follow-up" mode…
Label Dependent Deep Variational Paraphrase Generation
Siamak Shakeri, Abhinav Sethy
Generating paraphrases that are lexically similar but semantically different is a challenging task. Paraphrases of this form can be used to augment data sets for various NLP tasks…