5 papers · 1 filter
DACIP-RC: Domain Adaptive Continual Instruction Pre-Training via Reading Comprehension on Business Conversations
Elena Khasanova, Harsh Saini, Md Tahmid Rahman Laskar +3
The rapid advancements in Large Language Models (LLMs) have enabled their adoption in real-world industrial scenarios for various natural language processing tasks. However, the hi…
DACP: Domain-Adaptive Continual Pre-Training of Large Language Models for Phone Conversation Summarization
Xue-Yong Fu, Elena Khasanova, Md Tahmid Rahman Laskar +2
Large language models (LLMs) have achieved impressive performance in text summarization, yet their performance often falls short when applied to specialized domains that differ fro…
Can Post-Training Quantization Benefit from an Additional QLoRA Integration?
Xiliang Zhu, Elena Khasanova, Cheng Chen
Large language models (LLMs) have transformed natural language processing but pose significant challenges for real-world deployment. These models necessitate considerable computing…
Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization
Md Tahmid Rahman Laskar, Elena Khasanova, Xue-Yong Fu +2
This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query. When using L…
Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?
Xue-Yong Fu, Md Tahmid Rahman Laskar, Elena Khasanova +2
Large Language Models (LLMs) have demonstrated impressive capabilities to solve a wide range of tasks without being explicitly fine-tuned on task-specific datasets. However, deploy…