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It is time-saving to build a reading assistant for customer service representations (CSRs) when reading user manuals, especially information-rich ones. Current solutions don't fit the online custom service scenarios well due to the lack of attention
Externí odkaz:
http://arxiv.org/abs/2408.03633
Autor:
Zhang, Tong, Qin, Peixin, Deng, Yang, Huang, Chen, Lei, Wenqiang, Liu, Junhong, Jin, Dingnan, Liang, Hongru, Chua, Tat-Seng
Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown, ultimately risking user trust and satisfaction. To this e
Externí odkaz:
http://arxiv.org/abs/2405.12063
Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-s
Externí odkaz:
http://arxiv.org/abs/2405.12059
Publikováno v:
Findings of the Association for Computational Linguistics: ACL 2023. (2023)
The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However, current methods have trouble answering complex questions. Therefore, we introduce the Knowing-how & Knowing-that task that requires the model to
Externí odkaz:
http://arxiv.org/abs/2306.04187
The machine reading comprehension (MRC) of user manuals has huge potential in customer service. However,current methods have trouble answering complex questions. Therefore, we introduce the Knowing-how & Knowing-that task that requires the model to a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7640b136e96e06d703f0ba76d53fbf5b