Struc-Bench: Are Large Language Models Really Good at Generating Complex
  Structured Data?
Authors: name: Xiangru Tang, name: Yiming Zong, name: Yilun Zhao, name: Arman Cohan, name: Mark Gerstein

Despite the power of Large Language Models (LLMs) like GPT-4, they still
struggle with tasks that require generating complex, structured outputs. In
this study, we assess the capability of Current LLMs in generating complex
structured data and propose a structure-aware fine-tuning approach as a
solution to improve this ability. To perform a comprehensive evaluation, we
propose Struc-Bench, include five representative LLMs (i.e., GPT-NeoX 20B,
GPT-3.5, GPT-4, and Vicuna) and evaluate them on our carefully constructed
datasets spanning raw text, HTML, and LaTeX tables. Based on our analysis of
current model performance, we identify specific common formatting errors and
areas of potential improvement. To address complex formatting requirements, we
utilize FormatCoT (Chain-of-Thought) to generate format instructions from
target outputs. Our experiments show that our structure-aware fine-tuning
method, when applied to LLaMA-7B, significantly improves adherence to natural
language constraints, outperforming other evaluated LLMs. Based on these
results, we present an ability map of model capabilities from six dimensions
(i.e., coverage, formatting, reasoning, comprehension, pragmatics, and
hallucination). This map highlights the weaknesses of LLMs in handling complex
structured outputs and suggests promising directions for future work. Our code
and models can be found at https://github.com/gersteinlab/Struc-Bench.