Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space

Abstract: LLMs are used in chatbots or AI assistants to hold conversations with a human user. In such applications, the quality (e.g., user engagement, safety) of a conversation is important and can only be exactly known at the end of the conversation. To maximize its expected quality, conversation planning reasons about the stochastic transitions within a conversation to select the optimal LLM response at each turn. Existing simulation-based conversation planning algorithms typically select the optimal response by simulating future conversations with a large number of LLM queries at every turn. However, this process is extremely time-consuming and hence impractical for real-time conversations. This paper presents a novel approach called Semantic space COnversation Planning with improved Efficiency (SCOPE) that exploits the dense semantic representation of conversations to perform conversation planning efficiently. In particular, SCOPE models the stochastic transitions in conversation semantics and their associated rewards to plan entirely within the semantic space. This allows us to select the optimal LLM response at every conversation turn without needing additional LLM queries for simulation. As a result, SCOPE can perform conversation planning 70 times faster than conventional simulation-based planning algorithms when applied to a wide variety of conversation starters and two reward functions seen in the real world, yet achieving a higher reward within a practical planning budget. Our code can be found at: https://github.com/chenzhiliang94/convo-plan-SCOPE.

Overview of "Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs using Semantic Space"

In the paper titled "Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs using Semantic Space," the authors propose a novel framework called SCOPE, which stands for Semantic Space COconversation Planning with improved Efficiency. This framework aims to enhance real-time conversational abilities of large language models (LLMs) by choosing responses during conversations that maximize expected quality over time. The approach addresses limitations of conventional methods by focusing on efficient planning without requiring multiple costly LLM queries.

Problem Definition and Challenges

The primary task is optimizing conversation quality, which can only be fully assessed by the cumulative reward at the end of an interaction. Traditional methods improve conversation quality by selecting the most promising response at each turn, but they largely rely on simulating numerous potential future exchanges, a process that is computation-intensive and often infeasible in real-time scenarios. This paper overcomes this limitation by introducing a semantic space-based planning approach to perform these evaluations more efficiently.

Methodology: SCOPE

SCOPE leverages dense semantic representations of conversation states and actions, which enables the framework to perform much of the conversation planning in a continuous semantic space. Key components include:

1. Semantic Representation: A semantic embedding model transforms conversation states into dense vectors. This mapping creates a semantic space that captures the nuances of conversation turns.

2. Transition and Reward Models: Rather than relying on LLM simulations to characterize human responses, the authors train a stochastic transition model to simulate transitions and an associated reward model in this semantic space. The transition model understands how conversation semantically evolves, while the reward model estimates the reward tied to semantic states.

3. Planning in Semantic Space: With these models, SCOPE applies Monte Carlo tree search (MCTS), operating entirely within semantic space to predict the optimal lifecycle of a conversation without real-time LLM queries. This is a significant departure from existing approaches that heavily depend on LLM-powered simulations to achieve non-myopic decision-making.

Results

The empirical results demonstrate that SCOPE completes conversational planning approximately 70 times faster than traditional simulation methods, such as vanilla MCTS. Importantly, it achieves this while obtaining higher cumulative rewards under various predefined reward metrics, including engagement quantified by user token count and conversation safety. By simulating transitions within the semantic space rather than relying on actual or simulated proximal conversation outcomes, the SCOPE framework highlights an improvement in both efficacy and computational efficiency.

Implications and Future Directions

The implications of this research are notable for advancing the deployment of conversational agents at scale. Practically, SCOPE allows LLMs to engage in conversations more dynamically and efficiently, considering long-term conversational goals without the computational burden of heavy LLM simulations.

Theoretically, this approach suggests a novel way of integrating semantic representations into decision processes for LLMs, which could be expanded into other sequential decision-making or planning tasks in AI. In the future, enhancing the semantic embedding model to capture complex user-specific contexts or incorporate more nuanced reward shaping could further augment the capabilities of SCOPE. The paper also opens avenues for integrating personalized transition models that can dynamically adjust according to user demographics or preferences, potentially improving user satisfaction in conversational AI systems.

This research represents a meaningful step towards creating more adaptive and efficient conversation planning frameworks that leverage the strengths of semantic understanding, non-myopic planning, and real-time applicability.