Rethinking Conversational Agents in the Era of LLMs: Proactivity, Non-collaborativity, and Beyond

Paper · Source
Conversational Agents

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? How should AI agents balance proactive engagement with conversational respect? Why do language models struggle to implement user intent accurately from prompts? How do AI systems determine and balance multiple competing objectives? Does preference optimization undermine conversational grounding in language models? Can LLMs distinguish between linguistic form and semantic meaning? What causes coordination failures in multi-agent language model systems? How can agents discover and adapt to user preferences during conversation? How should humans and AI agents share control and decision-making? What structural patterns sustain successful multi-turn dialogue and prevent breakdown? How should systems validate code that agents generate? What makes agent memory systems durable and reusable across sessions? Can AI chatbots provide mental health support without reinforcing harmful beliefs? Should GUI agents use structured screen representations instead of end-to-end vision? What limits language model accuracy in evaluating ideas?