Original Paper: https://arxiv.org/abs/2302.09185
By: Albert Lu, Hongxin Zhang, Yanzhe Zhang, Xuezhi Wang, Diyi Yang
Abstract:
The limits of open-ended generative models are unclear, yet increasingly important. What causes them to succeed and what causes them to fail? In this paper, we take a prompt-centric approach to analyzing and bounding the abilities of open-ended generative models. We present a generic methodology of analysis with two challenging prompt constraint types: structural and stylistic. These constraint types are categorized into a set of well-defined constraints that are analyzable by a single prompt. We then systematically create a diverse set of simple, natural, and useful prompts to robustly analyze each individual constraint. Using the GPT-3 text-davinci-002 model as a case study, we generate outputs from our collection of prompts and analyze the model's generative failures. We also show the generalizability of our proposed method on other large models like BLOOM and OPT. Our results and our in-context mitigation strategies reveal open challenges for future research. We have publicly released our code at
In the fast-paced field of Natural Language Processing (NLP), Large Language Models (LLMs) such as GPT-3, BLOOM, and OPT are leading breakthroughs and innovations.
Yet, the challenge of how these models handle specific, constrained prompts remains less explored. This post dives into recent findings on this topic, providing AI Engineers in enterprise settings with key insights and strategies to optimize LLM effectiveness.
LLMs have revolutionized how machines understand and generate text that closely mimics human language, paving the way for creative solutions across various sectors.
Recognizing both the strengths and limitations of these models, particularly in response to constrained prompts, is vital as they become more embedded in our tech landscape.