Opportunities and Risks of LLMs for Scalable Deliberation with Polis
Polis is a platform that leverages machine intelligence to scale up
deliberative processes. In this paper, we explore the opportunities and risks
associated with applying Large Language Models (LLMs) towards challenges with
facilitating, moderating and summarizing the results of Polis engagements. In
particular, we demonstrate with pilot experiments using Anthropic's Claude that
LLMs can indeed augment human intelligence to help more efficiently run Polis
conversations. In particular, we find that summarization capabilities enable
categorically new methods with immense promise to empower the public in
collective meaning-making exercises. And notably, LLM context limitations have
a significant impact on insight and quality of these results.
  However, these opportunities come with risks. We discuss some of these risks,
as well as principles and techniques for characterizing and mitigating them,
and the implications for other deliberative or political systems that may
employ LLMs. Finally, we conclude with several open future research directions
for augmenting tools like Polis with LLMs.
6493b9c5b9bf46518a8a07f6
Christopher T. Small
claimed_verified
2023-06-23T07:32:00.347Z
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Christopher T Small
user
csmall
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Ivan Vendrov
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Esin Durmus
claimed_verified
2023-06-23T07:32:04.815Z
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Esin Durmus
user
esdurmus
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Hadjar Homaei
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Elizabeth Barry
claimed_verified
2023-06-27T07:28:02.214Z
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Elizabeth
user
lizbarry
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Julien Cornebise
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Ted Suzman
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Deep Ganguli
admin_assigned
2023-06-22T08:43:01.566Z
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Deep Ganguli
user
dganguli
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Colin Megill
Opportunities and Risks of LLMs for Scalable Deliberation with Polis
Polis is a platform that leverages machine intelligence to scale up
deliberative processes. In this paper, we explore the opportunities and risks
associated with applying Large Language Models (LLMs) towards challenges with
facilitating, moderating and summarizing the results of Polis engagements. In
particular, we demonstrate with pilot experiments using Anthropic's Claude that
LLMs can indeed augment human intelligence to help more efficiently run Polis
conversations. In particular, we find that summarization capabilities enable
categorically new methods with immense promise to empower the public in
collective meaning-making exercises. And notably, LLM context limitations have
a significant impact on insight and quality of these results.
  However, these opportunities come with risks. We discuss some of these risks,
as well as principles and techniques for characterizing and mitigating them,
and the implications for other deliberative or political systems that may
employ LLMs. Finally, we conclude with several open future research directions
for augmenting tools like Polis with LLMs.
Large Language Models enhance Polis conversations through summarization, but their context limitations pose risks that need to be managed.
Large Language Models LLMs Polis summarization context limitations deliberative processes collective meaning-making