Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond
as LLMs are trained to follow users’ instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent’s awareness of goals in a wide range of conversational systems, including proactive, non-collaborative, and multi-goal conversational systems.
Derived from the definition of proactivity in organizational behaviors [23] and its dictionary definitions, conversational agents’ proactivity can be defined as the capability to create or control the conversation by taking the initiative and anticipating impacts on themselves or human users.
Proactive ODD systems can consciously change topics [49] and lead directions [45, 48] for improving user engagement in the conversation. We will present the existing methods for topic shifting and planning in open-domain dialogues, including keyword-based discourse-level topic planning [45], graph-based topic planning [38, 52], and learning from interactions with users [28].
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
What prevents LLMs from applying their reasoning knowledge to improve outputs? What enables conversational agents to guide rather than just respond?- Can AI ever lead conversations without the anticipatory presence sustained attention provides?
- How does multi-turn conversation degrade AI intent alignment?
- Why do current conversational AI systems fail to develop shared vocabulary with users?
- Can AI learn when to speak in a conversation?
- Can timing and context awareness reduce the cognitive cost of AI suggestions?
- What would an AI trained for emancipatory reasoning look like?
- Can AI be used as a channel for human-initiated alarm?
- Can users articulate what they want before AI helps them discover it?
- Can parallel agents or complementary mechanisms replace single-human interrogation of LLMs?
- Why do LLM agents make promises without executing them?