4 citations · 10 across the 10 of their papers we have counts for
10 papers
Instruction-Driven Game Engine: A Poker Case Study
Hongqiu Wu, Xingyuan Liu, Yan Wang +1
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate g…
X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents
Weiqi Wu, Hongqiu Wu, Hai Zhao
The Turing test examines whether AIs exhibit human-like behaviour in natural language conversations. The traditional setting limits each participant to one message at a time and re…
Game Development as Human-LLM Interaction
Jiale Hong, Hongqiu Wu, Hai Zhao
Game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. Th…
Open Role-Playing with Delta-Engines
Hongqiu Wu, Zekai Xu, Tianyang Xu +5
Game roles can be reflections of personas from a parallel world. In this paper, we propose a new style of game-play to bridge self-expression and role-playing: \emph{open role-play…
From Role-Play to Drama-Interaction: An LLM Solution
Weiqi Wu, Hongqiu Wu, Lai Jiang +4
Drama is a form of storytelling inspired by human creativity, proceeding with a predefined storyline, carrying emotions and thoughts. This paper introduces \emph{LLM-based interact…
Instruction-Driven Game Engines on Large Language Models
Hongqiu Wu, Yan Wang, Xingyuan Liu +2
The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously gene…
Unveiling Vulnerability of Self-Attention
Khai Jiet Liong, Hongqiu Wu, Hai Zhao
Pre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipu…
Empower Nested Boolean Logic via Self-Supervised Curriculum Learning
Hongqiu Wu, Linfeng Liu, Hai Zhao +1
Beyond the great cognitive powers showcased by language models, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure…
Toward Adversarial Training on Contextualized Language Representation
Hongqiu Wu, Yongxiang Liu, Hanwen Shi +2
Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains f…
Forging Multiple Training Objectives for Pre-trained Language Models via Meta-Learning
Hongqiu Wu, Ruixue Ding, Hai Zhao +4
Multiple pre-training objectives fill the vacancy of the understanding capability of single-objective language modeling, which serves the ultimate purpose of pre-trained language m…