15 citations · 34 across the 12 of their papers we have counts for
7 papers · 1 filter
Incremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
Yisha Wu, Cen Mia Zhao, Yuanpei Cao +4
We introduce an incremental summarization system for customer support agents that intelligently determines when to generate concise bullet notes during conversations, reducing agen…
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
Zechun Liu, Barlas Oguz, Changsheng Zhao +6
Several post-training quantization methods have been applied to large language models (LLMs), and have been shown to perform well down to 8-bits. We find that these methods break d…
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
Borui Wang, Chengcheng Feng, Arjun Nair +6
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether…
Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
Asish Ghoshal, Arash Einolghozati, Ankit Arun +6
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In thi…
Bridging the Training-Inference Gap for Dense Phrase Retrieval
Gyuwan Kim, Jinhyuk Lee, Barlas Oguz +4
Building dense retrievers requires a series of standard procedures, including training and validating neural models and creating indexes for efficient search. However, these proced…