SDPO: Segment-Level Direct Preference Optimization for Social Agents
Social agents powered by large language models (LLMs) can simulate human
social behaviors but fall short in handling complex goal-oriented social
dialogues. Direct Preference Optimization (DPO) has proven effective in
aligning LLM behavior with human preferences across a variety of agent tasks.
Existing DPO-based approaches for multi-turn interactions are divided into
turn-level and session-level methods. The turn-level method is overly
fine-grained, focusing exclusively on individual turns, while session-level
methods are too coarse-grained, often introducing training noise. To address
these limitations, we propose Segment-Level Direct Preference Optimization
(SDPO), which focuses on specific key segments within interactions to optimize
multi-turn agent behavior while minimizing training noise. Evaluations on the
SOTOPIA benchmark demonstrate that SDPO-tuned agents consistently outperform
both existing DPO-based methods and proprietary LLMs like GPT-4o, underscoring
SDPO's potential to advance the social intelligence of LLM-based agents. We
release our code and data at
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO.
677b977e08c75e8046c38449
Aobo Kong
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2025-01-06T12:37:42.697Z
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Aobo Kong
user
KAB1314
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Wentao Ma
677b977e08c75e8046c3844b
Shiwan Zhao
extracted_confirmed
2025-01-06T12:16:57.707Z
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Shiwan Zhao
user
shiwan
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Yongbin Li
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Yuchuan Wu
677b977e08c75e8046c3844e
Ke Wang
677b977e08c75e8046c3844f
Xiaoqian Liu
677b977e08c75e8046c38450
Qicheng Li
677b977e08c75e8046c38451
Yong Qin
677b977e08c75e8046c38452
Fei Huang
SDPO: Segment-Level Direct Preference Optimization for Social Agents
Social agents powered by large language models (LLMs) can simulate human
social behaviors but fall short in handling complex goal-oriented social
dialogues. Direct Preference Optimization (DPO) has proven effective in
aligning LLM behavior with human preferences across a variety of agent tasks.
Existing DPO-based approaches for multi-turn interactions are divided into
turn-level and session-level methods. The turn-level method is overly
fine-grained, focusing exclusively on individual turns, while session-level
methods are too coarse-grained, often introducing training noise. To address
these limitations, we propose Segment-Level Direct Preference Optimization
(SDPO), which focuses on specific key segments within interactions to optimize
multi-turn agent behavior while minimizing training noise. Evaluations on the
SOTOPIA benchmark demonstrate that SDPO-tuned agents consistently outperform
both existing DPO-based methods and proprietary LLMs like GPT-4o, underscoring
SDPO's potential to advance the social intelligence of LLM-based agents. We
release our code and data at
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/SDPO.
Segment-Level Direct Preference Optimization (SDPO) enhances complex goal-oriented social dialogues in LLM-powered social agents by focusing on key segments, outperforming existing DPO methods.
large language models (LLMs) direct preference optimization (DPO) turn-level session-level segment-level SOTOPIA benchmark GPT-4o