Can a Single Model Master Both Multi-turn Conversations and Tool Use?
  CALM: A Unified Conversational Agentic Language Model
Authors: name: Emre Can Acikgoz, name: Jeremiah Greer, name: Akul Datta, name: Ze Yang, name: William Zeng, name: Oussama Elachqar, name: Emmanouil Koukoumidis, name: Dilek Hakkani-Tür, name: Gokhan Tur

Large Language Models (LLMs) with API-calling capabilities enabled building
effective Language Agents (LA), while also revolutionizing the conventional
task-oriented dialogue (TOD) paradigm. However, current approaches face a
critical dilemma: TOD systems are often trained on a limited set of target
APIs, requiring new data to maintain their quality when interfacing with new
services, while LAs are not trained to maintain user intent over multi-turn
conversations. Because both robust multi-turn management and advanced function
calling are crucial for effective conversational agents, we evaluate these
skills on three popular benchmarks: MultiWOZ 2.4 (TOD), BFCL V3 (LA), and
API-Bank (LA), and our analyses reveal that specialized approaches excel in one
domain but underperform in the other. To bridge this chasm, we introduce CALM
(Conversational Agentic Language Model), a unified approach that integrates
both conversational and agentic capabilities. We created CALM-IT, a carefully
constructed multi-task dataset that interleave multi-turn ReAct reasoning with
complex API usage. Using CALM-IT, we train three models CALM 8B, CALM 70B, and
CALM 405B, which outperform top domain-specific models, including GPT-4o,
across all three benchmarks.