Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024)

Abstract: Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adept at devising strategies and performing tasks, these agents struggle with seeking clarification and grasping precise user intentions. To bridge this gap, we introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users’ implicit intentions through explicit queries. Next, we propose the incorporation of model experts as the upstream in agent designs to enhance user-agent interaction. Employing IN3, we empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires about user intentions, and refines them into actionable goals before starting downstream agent task execution. Integrating it into the XAgent framework, we comprehensively evaluate the enhanced agent system regarding user instruction understanding and execution, revealing that our approach notably excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency.

## introduction large language models including the openai gpt @xcite , llama @xcite , and mistral series @xcite have made great strides in high-quality text and code generation @xcite @xcite , complex logical reasonings @xcite @xcite , and using external tools (schick et al., 2023; qin et al., 2023, * indicates equal contribution. current agent benchmarks usually assume the clearance of given tasks and exclude user intention understanding as an important aspect for evaluation. given this ignorance in assessment, we formulate intention-in-interaction (in3), a benchmark aiming to test the agent's interaction ability through explicit task vagueness judgment and user intention understanding. in3 provides general agent tasks over hundreds of categories, each with its vagueness, missing details, and each detail's importance level and options to inspire the user's true intentions. in addition, we propose an innovative approach to evaluate present agent systems, with metrics addressing the existing gap in evaluation by incorporating two key aspects that emphasize user participation: i) instruction understanding: to directly evaluate the process of intention understanding, we quantitatively measure the agent's judgment of task vagueness and recovery of important missing details during user-agent interaction. ii) instruction execution: to indirectly reflect the effectiveness of intention understanding, we contrast the agent task execution given the initial (vague) or finalized (clear) user goal. language models lie at the core of agent designs, but our preliminary test reveals that most of them seriously lack the initiative to actively make inquiries and comprehensively understand user intentions. this raises our research problem: how to enhance the interaction ability of the agent system through the underlying model's robust intention understanding? to address this, we propose to integrate a specialized upstream model in charge of user-agent interaction in agent design. as illustrated in figure 1 , to explicitly understand the user's specific intentions, the model should actively and explicitly ask users for missing details before passing the task for downstream execution. to enhance this interaction capability, we apply training split tasks in in3 to construct simulated model-user conversation records that provide explicit initial thoughts, rounds of queries with options, summarization of implicit intentions, and diverse user response tones. training on these conversations, we adapt mistral-7b into mistral-interact, a powerful and robust variant capable of judging the vagueness of user instruction, actively querying for missing details with suggestions, and explicitly summarizing the detailed and clear user intentions. in experiments, we incorporate mistral-interact into the xagent framework @xcite , an autonomous agent system for complex task solving. through comprehensive evaluation of both user instruction understanding and agent instruction execution, we reveal that our adapted mistral-interact can i) correctly judge the vagueness of over 85% tasks, recover over 70% of the most important missing details, and summarize over 96% implicit user intentions without omission, ii) significantly reduce the number of too general or unnecessary goals and lower the tool invocation times during agent execution, which raises overall agent efficiency. the performance of mistral-interact is more aligned with human preferences, far better than the previous llama and mistral series while rivaling closed-source gpt-4 but with a much smaller scale. our method proves the viability of integrating smaller, open-sourced model experts for robust implicit intention understanding during user-agent interaction, and we conclude by discussing its further implications and future directions. ## method to further enhance the implicit intention understanding ability of current agent designs, we propose to train a model expert specialized in implicit intention understanding through conversation with users and incorporate it as an upstream module in agent design. denote agent execution process as a function f , then this module should act as a "buffer" between initial user task t and downstream execution f (t). if t is already clear, the module should directly pass t for execution, while if t is vague, the module should robustly chat with the user, turning t into t user with specific user intentions. we expect f (t user ) to perform better and be more aligned with the user's intentions than f (t). to realize this, we first apply in3 to construct conversation records for training. using the constructed interaction data, we adapt mistral-7b into mistral-interact, a powerful variant capable of judging the vagueness of user instruction, actively querying for missing details with suggestions, and explicitly summarizing detailed user intentions. ## experiments an agent's intention understanding capability can be assessed directly through user interaction and indirectly through downstream task execution. interaction focuses on intention understanding itself, while execution focuses on intention understanding's ultimate goal, which is to enhance the agent design's efficiency. therefore, to comprehensively evaluate the effectiveness of an agent design capable of interaction, we divide our experiments into two aspects: i) instruction understanding: the evaluation of agent's intention understanding capability during user-agent interaction to directly show its excellence; ii) instruction execution: the evaluation of agent task performance with an upstream plug-in interaction expert to reflect its effectiveness. ## conclusion this work investigates the enhancement and evaluation of implicit intention understanding in agent designs. specifically, we introduce the intention-in-interaction (in3) benchmark to assess an agent's ability to understand implicit intentions from vague instructions. we also develop mistral-interact, an open-source model that precisely discerns task vagueness, seeks clarification, and captures user intentions. incorporating it into xagent, we demonstrate its efficacy in agent instruction understanding and executing. our work pioneers the integration of user participation and implicit intention understanding in agent designs, paving the way for more robust interaction benchmarks and systems.