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Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed agents and might necessitate considerable prompt tuning.
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Matthew Hausknecht, Prithviraj Ammanabrolu, Côté Marc-Alexandre, and Yuan Xingdi · 1909
Earlier work this paper cites.
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