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SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
Yuchen Lin · Yicheng Fu · Karina Yang · Prithviraj Ammanabrolu · Faeze Brahman · Shiyu Huang · Chandra Bhagavatula · Yejin Choi · Xiang Ren
Event URL: https://openreview.net/forum?id=FvAOJ67bmt »

We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks. SwiftSage integrates the strengths of behavior cloning and prompting large language models (LLMs) to enhance task completion performance. The framework comprises two primary modules: the Swift module, representing fast and intuitive thinking, and the Sage module, emulating deliberate thought processes. The Swift module is a small encoder-decoder LM fine-tuned on the oracle agent's action trajectories, while the Sage module employs LLMs such as GPT-4 for subgoal planning and grounding. We develop a heuristic method to harmoniously integrate the two modules, resulting in a more efficient and robust problem-solving process. In 30 tasks from the ScienceWorld benchmark, SwiftSage significantly outperforms other methods such as SayCan, ReAct, and Reflexion, demonstrating its effectiveness in solving complex real-world tasks.

Author Information

Yuchen Lin (Allen Institute for AI)
Yicheng Fu (Tsinghua University, Tsinghua University)
Karina Yang (University of Southern California)
Prithviraj Ammanabrolu (Allen Institute for Artificial Intelligence)
Faeze Brahman (Allen Institute for AI)
Shiyu Huang (Tsinghua University)
Chandra Bhagavatula (AI2)
Yejin Choi (University of Washington)
Xiang Ren (University of Southern California)

Xiang Ren joined the Department of Computer Science at USC as Assistant Professor in 2018. Previously, he was a visiting researcher at Stanford University. Xiang received his PhD in Computer Science at University of Illinois at Urbana-Champaign (2017), where he was a Google PhD Fellow and a Richard T. Cheng Fellow working with Prof. Jiawei Han. Xiang's research develops data-driven and machine learning methods for turning unstructured text data into machine-actionable structures. Xiang's research has been recognized with several prestigious awards including a Yahoo!-DAIS Research Excellence Award, a Yelp Dataset Challenge award, a C. W. Gear Outstanding Graduate Student Award and a David J. Kuck Outstanding M.S. Thesis Award. Technologies he developed has been transferred to US Army Research Lab, National Institute of Health, Microsoft, Yelp and TripAdvisor.

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