Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement

• URL: http://arxiv.org/abs/2402.10618v2
• Date: Sat, 15 Jun 2024 13:51:38 GMT
• Title: Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement
• Authors: Yihong Tang, Jiao Ou, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai,
• Abstract summary: Large Language Models (LLMs) have propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs) Existing LLM-based RPSs still struggle to align with roles when handling intricate and trapped queries in boundary scenarios. We design the Modular ORchestrated Trap-setting Interaction SystEm (MORTISE) to benchmark and improve the role-playing LLMs' performance.
• Score: 17.5855800570993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of Large Language Models (LLMs) has propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs). While enhanced with ordinary role-relevant training dialogues, existing LLM-based RPSs still struggle to align with roles when handling intricate and trapped queries in boundary scenarios. In this paper, we design the Modular ORchestrated Trap-setting Interaction SystEm (MORTISE) to benchmark and improve the role-playing LLMs' performance. MORTISE can produce highly role-relevant aggressive queries through the collaborative effort of multiple LLM-based modules, and formulate corresponding responses to create an adversarial training dataset via a consistent response generator. We select 190 Chinese and English roles to construct aggressive queries to benchmark existing role-playing LLMs. Through comprehensive evaluation, we find that existing models exhibit a general deficiency in role alignment capabilities. We further select 180 of the roles to collect an adversarial training dataset (named RoleAD) and retain the other 10 roles for testing. Experiments on models improved by RoleAD indicate that our adversarial dataset ameliorates this deficiency, with the improvements demonstrating a degree of generalizability in ordinary scenarios.

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