Published on Sep 16·Featured in Daily Papers on Sep 18Authors:Xiangru Tang,Yiming Zong,Yilun Zhao,Arman Cohan,Mark Gerstein
Abstract
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.
Commentary
The paper "Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?" targets a significant challenge in the domain of Large Language Models (LLMs) — generating complex structured outputs.
Key Takeaways:
Problem Identification: Despite the prowess of modern LLMs, their ability to generate structured outputs like tables, HTML, or LaTeX, remains challenging.
Struc-Bench Benchmark: This work introduces a comprehensive benchmark to assess the capability of LLMs in generating structured data, evaluating several state-of-the-art models across various structured formats.
Detailed Analysis: The researchers identify common formatting errors in LLM outputs, providing insights into the weaknesses of these models when tasked with generating structured content.
Structure-Aware Fine-tuning: A novel method, FormatCoT, is introduced to generate format instructions from target outputs, improving the model's adherence to complex structural requirements.
Ability Map: The authors present a map detailing model capabilities across six dimensions, highlighting areas of strengths and weaknesses.
Potential Real-World Impact:
Complex Output Generation: The findings and proposed solutions can advance LLMs' abilities to generate complex structured outputs, vital for tasks like document generation, website design, and data table creation.
Better Model Evaluations: Struc-Bench can become a standard benchmark for future models, ensuring they're tested for their capabilities in structured output generation.
Structured Data in Applications: Improved capabilities in structured data generation can enhance applications like automatic report writing, code generation, or content management systems.
Guidance for Future Research: The identification of common errors and the ability map will guide researchers on where to focus their efforts.
Challenges:
Complexity: While the proposed methods show promise, there's inherent complexity in generating structured content, and perfecting this will remain a challenge.
Adoption: The broader impact would depend on how the community adopts the benchmark and the structure-aware fine-tuning method in their research and applications.
Considering the critical nature of generating structured data and the potential implications of improving this ability in LLMs:
I'd rate the real-world impact of this paper as a 9 out of 10.
The introduction of a structured data benchmark, insights into model errors, and a novel fine-tuning approach have the potential to push the boundaries of what LLMs can achieve in real-world structured data generation tasks.
