BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
The massive successes of large language models (LLMs) encourage the emerging
exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to
generate actions with its core LLM and interact with environments, which
facilitates the ability to resolve complex tasks by conditioning on past
interactions such as observations and actions. Since the investigation of LAA
is still very recent, limited explorations are available. Therefore, we provide
a comprehensive comparison of LAA in terms of both agent architectures and LLM
backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs
such that each labor LAA focuses on one type of action, i.e. BOLAA,
where a controller manages the communication among multiple agents. We conduct
simulations on both decision-making and multi-step reasoning environments,
which comprehensively justify the capacity of LAAs. Our performance results
provide quantitative suggestions for designing LAA architectures and the
optimal choice of LLMs, as well as the compatibility of both. We release our
implementation code of LAAs to the public at
https://github.com/salesforce/BOLAA.
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Caiming Xiong
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Silvio Savarese
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
The massive successes of large language models (LLMs) encourage the emerging
exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to
generate actions with its core LLM and interact with environments, which
facilitates the ability to resolve complex tasks by conditioning on past
interactions such as observations and actions. Since the investigation of LAA
is still very recent, limited explorations are available. Therefore, we provide
a comprehensive comparison of LAA in terms of both agent architectures and LLM
backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs
such that each labor LAA focuses on one type of action, i.e. BOLAA,
where a controller manages the communication among multiple agents. We conduct
simulations on both decision-making and multi-step reasoning environments,
which comprehensively justify the capacity of LAAs. Our performance results
provide quantitative suggestions for designing LAA architectures and the
optimal choice of LLMs, as well as the compatibility of both. We release our
implementation code of LAAs to the public at
https://github.com/salesforce/BOLAA.
A comprehensive comparison and new orchestration strategy for LLM-augmented Autonomous Agents (LAAs) demonstrate their effectiveness in decision-making and multi-step reasoning tasks.
large language models (LLMs) LLM-augmented Autonomous Agents (LAAs) agent architectures LLM backbones BOLAA decision-making multi-step reasoning