22 citations · 42 across the 10 of their papers we have counts for
7 papers · 1 filter
Post-training an LLM for RAG? Train on Self-Generated Demonstrations
Matthew Finlayson, Ilia Kulikov, Daniel M. Bikel +3
Large language models (LLMs) often struggle with knowledge intensive NLP tasks, such as answering "Who won the latest World Cup?" because the knowledge they learn during training m…
Binary and Ternary Natural Language Generation
Zechun Liu, Barlas Oguz, Aasish Pappu +2
Ternary and binary neural networks enable multiplication-free computation and promise multiple orders of magnitude efficiency gains over full-precision networks if implemented on s…
Mixture-of-Supernets: Improving Weight-Sharing Supernet Training with Architecture-Routed Mixture-of-Experts
Ganesh Jawahar, Haichuan Yang, Yunyang Xiong +10
Weight-sharing supernets are crucial for performance estimation in cutting-edge neural architecture search (NAS) frameworks. Despite their ability to generate diverse subnetworks w…
Detecting Extraneous Content in Podcasts
Sravana Reddy, Yongze Yu, Aasish Pappu +3
Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers th…
The Spotify Podcast Dataset
Ann Clifton, Aasish Pappu, Sravana Reddy +4
Podcasts are a relatively new form of audio media. Episodes appear on a regular cadence, and come in many different formats and levels of formality. They can be formal news journal…
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Sheng Chen, Akshay Soni, Aasish Pappu +1
Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit sev…
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
Dragomir Radev, Amanda Stent, Joel Tetreault +8
The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one…