14 citations · 42 across the 18 of their papers we have counts for
4 papers · 1 filter
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings
Xuanqing Liu, Luyang Kong, Wei Niu +6
Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. However, analyzing live d…
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
DynaMaR: Dynamic Prompt with Mask Token Representation
Xiaodi Sun, Sunny Rajagopalan, Priyanka Nigam +4
Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically wh…
Magic Pyramid: Accelerating Inference with Early Exiting and Token Pruning
Xuanli He, Iman Keivanloo, Yi Xu +4
Pre-training and then fine-tuning large language models is commonly used to achieve state-of-the-art performance in natural language processing (NLP) tasks. However, most pre-train…