Designing LLM Chains by Adapting Techniques from Crowdsourcing Workflows
CoRR(2023)
摘要
LLM chains enable complex tasks by decomposing work into a sequence of
sub-tasks. Crowdsourcing workflows similarly decompose complex tasks into
smaller tasks for human crowdworkers. Chains address LLM errors analogously to
the way crowdsourcing workflows address human error. To characterize
opportunities for LLM chaining, we survey 107 papers across the crowdsourcing
and chaining literature to construct a design space for chain development. The
design space connects an LLM designer's objectives to strategies they can use
to achieve those objectives, and tactics to implement each strategy. To explore
how techniques from crowdsourcing may apply to chaining, we adapt crowdsourcing
workflows to implement LLM chains across three case studies: creating a
taxonomy, shortening text, and writing a short story. From the design space and
our case studies, we identify which techniques transfer from crowdsourcing to
LLM chaining and raise implications for future research and development.
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