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pro vyhledávání: '"Fu, Xue"'
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 Large Language Models (LLMs) for this task, usually a new call to the L
Externí odkaz:
http://arxiv.org/abs/2403.00067
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, deploying LLMs in the real world is not trivial, as it requires substantial
Externí odkaz:
http://arxiv.org/abs/2402.00841
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs
In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as evaluators for te
Externí odkaz:
http://arxiv.org/abs/2311.00681
This paper studies how to effectively build meeting summarization systems for real-world usage using large language models (LLMs). For this purpose, we conduct an extensive evaluation and comparison of various closed-source and open-source LLMs, name
Externí odkaz:
http://arxiv.org/abs/2310.19233
AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching
Autor:
Laskar, Md Tahmid Rahman, Chen, Cheng, Fu, Xue-Yong, Azizi, Mahsa, Bhushan, Shashi, Corston-Oliver, Simon
In recent years, the utilization of Artificial Intelligence (AI) in the contact center industry is on the rise. One area where AI can have a significant impact is in the coaching of contact center agents. By analyzing call transcripts using Natural L
Externí odkaz:
http://arxiv.org/abs/2305.17619
Autor:
Zhao, Zinan, Hu, Ding, Fu, Xue, Zhou, Kaijuan, Gu, Yanhong, Tan, Guotai, Lu, Xingye, Dai, Pengcheng
An experimental determination of electronic phase diagrams of high-transition temperature (high-$T_c$) superconductors forms the basis for a microscopic understanding of unconventional superconductivity. For most high-$T_c$ superconductors, the elect
Externí odkaz:
http://arxiv.org/abs/2305.04424
Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from
Externí odkaz:
http://arxiv.org/abs/2212.00252
Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications. Existing deep learning-based RED methods have been proposed under the friendly environments. However, these methods perform unstable under low si
Externí odkaz:
http://arxiv.org/abs/2212.00242
Specific emitter identification (SEI) plays an increasingly crucial and potential role in both military and civilian scenarios. It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from giv
Externí odkaz:
http://arxiv.org/abs/2211.15379
Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but also such data may have lots of annotation errors even after the annota
Externí odkaz:
http://arxiv.org/abs/2211.01354