@inproceedings{chen-etal-2025-tcqa2,
title = "{TCQA}$^2$: A Tiered Conversational {Q}{\&}{A} Agent in Gaming",
author = "Chen, Ze and
Wei, Chengcheng and
Zheng, Jiewen and
He, Jiarong",
editor = "Kamalloo, Ehsan and
Gontier, Nicolas and
Lu, Xing Han and
Dziri, Nouha and
Murty, Shikhar and
Lacoste, Alexandre",
booktitle = "Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.realm-1.20/",
doi = "10.18653/v1/2025.realm-1.20",
pages = "289--297",
ISBN = "979-8-89176-264-0",
abstract = "This paper focuses on intelligent Q{\&}A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content. It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools. We propose a Tiered Conversational Q{\&}A Agent (TCQA$^2$), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses. Parallel modules in each tier cut latency via distributed tasks. Multiple retrievers and short-term memory boost multi-turn Q{\&}A. Hallucination and safety checks improve response quality. Player tags and long-term memory enable personalization. Real-world evaluations show TCQA$^2$ outperforms prompt-engineered LLMs and RAG-based agents in gaming Q{\&}A, personalized dialogue, and risk mitigation."
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%0 Conference Proceedings
%T TCQA²: A Tiered Conversational Q&A Agent in Gaming
%A Chen, Ze
%A Wei, Chengcheng
%A Zheng, Jiewen
%A He, Jiarong
%Y Kamalloo, Ehsan
%Y Gontier, Nicolas
%Y Lu, Xing Han
%Y Dziri, Nouha
%Y Murty, Shikhar
%Y Lacoste, Alexandre
%S Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-264-0
%F chen-etal-2025-tcqa2
%X This paper focuses on intelligent Q&A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content. It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools. We propose a Tiered Conversational Q&A Agent (TCQA²), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses. Parallel modules in each tier cut latency via distributed tasks. Multiple retrievers and short-term memory boost multi-turn Q&A. Hallucination and safety checks improve response quality. Player tags and long-term memory enable personalization. Real-world evaluations show TCQA² outperforms prompt-engineered LLMs and RAG-based agents in gaming Q&A, personalized dialogue, and risk mitigation.
%R 10.18653/v1/2025.realm-1.20
%U https://aclanthology.org/2025.realm-1.20/
%U https://doi.org/10.18653/v1/2025.realm-1.20
%P 289-297
Markdown (Informal)
[TCQA2: A Tiered Conversational Q&A Agent in Gaming](https://aclanthology.org/2025.realm-1.20/) (Chen et al., REALM 2025)
ACL
- Ze Chen, Chengcheng Wei, Jiewen Zheng, and Jiarong He. 2025. TCQA2: A Tiered Conversational Q&A Agent in Gaming. In Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025), pages 289–297, Vienna, Austria. Association for Computational Linguistics.