Can a Single Model Master Both Multi-turn Conversations and Tool Use?
  CALM: A Unified Conversational Agentic Language Model
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.
67aece59f2e8a2ee35b5affd
Emre Can Acikgoz
claimed_verified
2025-02-18T09:34:01.421Z
63888d3fd68e37abd599f428
https://cdn-avatars.huggingface.co/v1/production/uploads/63888d3fd68e37abd599f428/YaNyxG_oM6IgrHTkFZ6Eq.jpeg
emre can
user
emrecanacikgoz
67aece59f2e8a2ee35b5affe
Jeremiah Greer
67aece59f2e8a2ee35b5afff
Akul Datta
67aece59f2e8a2ee35b5b000
Ze Yang
67aece59f2e8a2ee35b5b001
William Zeng
67aece59f2e8a2ee35b5b002
Oussama Elachqar
67aece59f2e8a2ee35b5b003
Emmanouil Koukoumidis
67aece59f2e8a2ee35b5b004
Dilek Hakkani-Tür
claimed_verified
2025-04-02T19:55:42.162Z
6747519282137543033451d9
/avatars/8bd763f19d8e9f97f0f6817435b062dc.svg
Dilek Hakkani-Tur
user
dilekht
67aece59f2e8a2ee35b5b005
Gokhan Tur
Can a Single Model Master Both Multi-turn Conversations and Tool Use?
  CALM: A Unified Conversational Agentic Language Model
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.
A unified conversational language model, CALM, enhances both multi-turn conversation management and API usage by leveraging a multi-task dataset, outperforming specialized models across various benchmarks.
Language Agents Task-oriented dialogue multi-turn management API-calling capabilities ReAct reasoning MultiWOZ 2.4 BFCL V3 API-Bank Conversational Agentic Language Model CALM-IT GPT-4o