Evaluating Large Language Models on Controlled Generation Tasks

• URL: http://arxiv.org/abs/2310.14542v1
• Date: Mon, 23 Oct 2023 03:48:24 GMT
• Title: Evaluating Large Language Models on Controlled Generation Tasks
• Authors: Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Frederick Wieting, Nanyun Peng, Xuezhe Ma
• Abstract summary: We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models.
• Score: 92.64781370921486
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks. We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models. We conclude that **large language models struggle at meeting fine-grained hard constraints**.

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