bibtext: @inproceedings{
  shi2024chops,
  title={{CHOPS}: {CH}at with custOmer Profile Systems for Customer Service with {LLM}s},
  author={Jingzhe Shi and Jialuo Li and Qinwei Ma and Zaiwen Yang and Huan Ma and Lei Li},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=9Wmdk94oKF}
  }
abstract: Businesses and software platforms are increasingly utilizing Large Language Models (LLMs) like GPT-3.5, GPT-4, GLM-3, and LLaMa-2 as chat assistants with file access or as reasoning agents for custom service. Current LLM-based customer service models exhibit limited integration with customer profiles and lack operational capabilities, while existing API integrations prioritize diversity over precision and error avoidance that are crucial in real-world scenarios for Customer Service. We propose an LLMs agent called **CHOPS** (**CH**at with cust**O**mer **P**rofile in existing **S**ystem) that: (1) efficiently utilizes existing databases or systems to access user information or interact with these systems based on existing guidance; (2) provides accurate and reasonable responses or executing required operations in the system while avoiding harmful operations; and (3) leverages the combination of small and large LLMs together to provide satisfying performance while having decent inference cost. We introduce a practical dataset, *CPHOS-dataset*, including a database, some guiding files, and QA pairs collected from *CPHOS*, which employs an online platform to facilitate the organization of simulated Physics Olympiads for high school teachers and students. We conduct extensive experiments to validate the performance of our proposed **CHOPS** architecture using the *CPHOS-dataset*, aiming to demonstrate how LLMs can enhance or serve as alternatives to human customer service.
title: CHOPS: CHat with custOmer Profile Systems for Customer Service with LLMs
authors: Jingzhe Shi Jialuo Li Qinwei Ma Zaiwen Yang Huan Ma Lei Li