Language agents that communicate effectively with fewer words
Towards Communication-Efficient Social Intelligence in Language Agents
Computation and Language
Summary
Socially smart language agents need to cooperate by sharing just the right amount of information, so conversations stay useful without being long or confusing. The authors created a method called Teacher-Assisted Communication Training (TACT) that teaches these agents to pick their words and actions carefully, helping them meet goals while using fewer words. TACT works by checking different ways of saying something, guessing how the partner might respond, and then learning which version works best with the smallest cost in words. The authors tested TACT in two settings and found it improved success rates while cutting down on unnecessary communication.
What this means in practice
- •For conversational ai developers: Create dialogue agents that achieve goals faster by optimizing communication with users using TACT.
- •For virtual assistant teams: Improve virtual assistants’ ability to negotiate and coordinate by reducing needless dialog turns with TACT-trained strategies.
Authors
Linxiao Gong, Yijie Xu, Tianfu Wang, Yin Wu, Yili Wang, Xingbo Yao, Huizai Yao, Xilin Xia, Haowen Yang, Hui Xiong
Abstract
Socially intelligent language agents must negotiate, coordinate, and resolve conflicting preferences while respecting the time and attention of both participants. Balancing these demands is challenging because agents must convey enough to address a partner's constraints and advance their goals without adding words that do not help the interaction. In this paper, we propose Teacher-Assisted Communication Training (TACT) to improve social goal attainment while reducing communication cost, making interactions with agents more productive and less demanding. We first characterize communication efficiency in terms of action strategy and expression, whose effects extend beyond the current utterance to the partner's response and subsequent exchanges. We design TACT to revise student-generated actions, test the revisions through partner responses, and distill useful feedback into the student. An expression specialist removes unnecessary detail while preserving the intended action, while a strategy specialist proposes alternatives that may better address the partner's constraints. To determine which revision helps, TACT samples a partner response for each candidate and selects a teacher reference by balancing local goal support against action-token cost. That reference guides on-policy distillation on the student's own generation prefixes, allowing the student to act independently at deployment. We evaluate TACT on SOTOPIA and AgentSense. On SOTOPIA, it achieves the highest Goal among the evaluated methods on All and Hard while using substantially fewer target tokens than SFT+SDPO. On AgentSense, it improves goal success over the initial student while reducing target tokens and interaction messages.