Papers with customer service agents

4 papers
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features (N19-2)

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Challenge: e-commerce companies often have the option of escalating complaints by filing grievances with a government authority . this is detrimental to an ecommerce company, but this problem is challenging to solve by integrating recurrent neural networks with manually-engineered features.
Approach: They propose a model that integrates recurrent neural networks with manually-engineered features to identify cases where the customer expresses such an intent.
Outcome: The proposed model outperforms baseline models and provides better recall and triage for specialized agents.
Improving Knowledge Production Efficiency With Question Answering on Conversation (2023.acl-industry)

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Challenge: Existing researches on conversation-based QA focus on document-based tasks . current researche focuses on document based tasks, but there is a lack of researche on conversation based qa .
Approach: They propose a multi-span extraction model on conversation-based QA and introduce continual pre-training and multi-task learning schemes to further improve model performance.
Outcome: The proposed model outperforms baseline on two Chinese datasets and will be released for research purposes.
The economic trade-offs of large language models: A case study (2023.acl-industry)

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Challenge: Large Language Models (LLMs) are a natural fit for contact-based customer service, but their efficacy must be balanced with the cost of training and serving them.
Approach: They propose a cost framework for evaluating an NLP model’s utility for the enterprise as a function of the usefulness of the responses that they generate.
Outcome: The proposed model can be used to help human agents handle complex customer service calls and can be modified to improve their performance.
TelBench: A Benchmark for Evaluating Telco-Specific Large Language Models (2024.emnlp-industry)

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Challenge: a growing demand for Large Language Models (LLMs) is requiring specialized models to augment customer service agents' skills.
Approach: They propose a methodology for developing a specialized Telecommunications LLM . they use a dataset to evaluate customer service expertise in the telecommunications domain .
Outcome: The proposed model improves the efficiency of customer service agents and reduces response times.

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