The case study talks about a company RAKT, which recently "implemented a language model chatbot to handle customer queries". As a student intern, it's your job "to investigate the issues and recommend improvements for the chatbot".
Latency: the chatbot's response time is slow and detracts from the customer experience.
Linguistic Nuances: the chatbot's language model is struggling to respond appropriately to ambiguous statements.
Architecture: the chatbot's architecture is too simplistic and unable to handle complex language.
Dataset: the chatbot's training dataset is not diverse enough, leading to poor accuracy in understanding and responding to customer queries.
Processing power: the system's computational capacity is a limiting factor.
Ethical challenges: the chatbot does not always give appropriate advice and is prone to revealing personal information from its training dataset.
The chatbot uses a natural language processing model, however it has a high latency (slow processing time).
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