4 accepted papers
Maintaining role-awareness in Role-Playing Conversational Agents (RPCAs) is a significant challenging, largely because the creative nature of role-playing makes it difficult to design verifiable reward signals for reinforcement learning (RL). To address this, we propose VeriRole, a new framework des…
Large Language Models (LLMs) have demonstrated significant advancements in various fields, notably in Role-Playing Conversational Agents (RPCAs). However, when confronted with role-specific professional inquiries, LLMs-based RPCAs tend to underperform due to their excessive emphasis on the conversat
As Large-scale Language Models (LLMs) advance, the development of engaging Role-Playing Conversational Agents (RPCAs) has gained prominence. Despite this progress, there is a notable absence of benchmarks designed around dialogues, rather than question-answering formats, to assess the effectiveness…
Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language visual and acoustic modalities. The inherent intent ambiguity makes it challenging to recognize in multimodal scenarios. Existing MIR methods tend to model the individual video independently ignoring global c…