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The limits of open-ended generative models are unclear, yet increasinglyimportant. What causes them to succeed and what causes them to fail? In thispaper, we take a prompt-centric approach to analyzing and bounding theabilities of open-ended generative models. We present a generic methodology ofanalysis with two challenging prompt constraint types: structural andstylistic. These constraint types are categorized into a set of well-definedconstraints that are analyzable by a single prompt. We then systematicallycreate a diverse set of simple, natural, and useful prompts to robustly analyzeeach individual constraint. Using the GPT-3 text-davinci-002 model as a casestudy, we generate outputs from our collection of prompts and analyze themodel's generative failures. We also show the generalizability of our proposedmethod on other large models like BLOOM and OPT. Our results and our in-contextmitigation strategies reveal open challenges for future research. We havepublicly released our code at https://github.com/SALT-NLP/Bound-Cap-LLM.

Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints