3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Leveraging LLMs for Dialogue Quality Measurement
Jinghan Jia, Abi Komma, Timothy Leffel +5
In task-oriented conversational AI evaluation, unsupervised methods poorly correlate with human judgments, and supervised approaches lack generalization. Recent advances in large l…
Prompt Perturbation Consistency Learning for Robust Language Models
Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi +4
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However,…
Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models
Neal Lawton, Anoop Kumar, Govind Thattai +2
Parameter-efficient tuning (PET) methods fit pre-trained language models (PLMs) to downstream tasks by either computing a small compressed update for a subset of model parameters,…
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
Kuan-Hao Huang, Varun Iyer, I-Hung Hsu +3
Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cos…
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations
Kuan-Hao Huang, Varun Iyer, Anoop Kumar +3
Syntactically controlled paraphrase generation has become an emerging research direction in recent years. Most existing approaches require annotated paraphrase pairs for training a…