Knowledge-Centric Templatic Views of Documents

Abstract: Authors seeking to communicate with broader audiences often share their ideas in various document formats, such as slide decks, newsletters, reports, and posters. Prior work on document generation has generally tackled the creation of each separate format to be a different task, leading to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. We consider each of these documents as templatic views of the same underlying knowledge/content, and we aim to unify the generation and evaluation of these templatic views. We begin by showing that current LLMs are capable of generating various document formats with little to no supervision. Further, a simple augmentation involving a structured intermediate representation can improve performance, especially for smaller models. We then introduce a novel unified evaluation framework that can be adapted to measuring the quality of document generators for heterogeneous downstream applications. This evaluation is adaptable to a range of user defined criteria and application scenarios, obviating the need for task specific evaluation metrics. Finally, we conduct a human evaluation, which shows that people prefer 82% of the documents generated with our method, while correlating more highly with our unified evaluation framework than prior metrics in the literature.

• The paper introduces the SURe method, which leverages LLMs to create structured templatic views from documents in a zero-shot setting.
• It proposes a template-agnostic evaluation framework combining metrics like ROUGE and BERTScore to assess document quality consistently.
• Empirical results show an 82% preference for documents generated via SURe, highlighting superior formatting and clarity over traditional methods.

Knowledge-Centric Templatic Views of Documents

Introduction

The paper "Knowledge-Centric Templatic Views of Documents" (2401.06945) centers on a method that addresses the inefficiencies inherent in the generation and evaluation of document views. Traditional methods often involve independent systems for different document types, leading to fragmented learning and disjointed evaluations. This research presents a unified approach to document generation that leverages LLMs to produce templatic views, streamlining both the creation and evaluation processes.

Methodology

The central innovation presented in this paper is the development of a Structure Unified Representation (SURe), a format that encapsulates essential information from an input document, suitable for generating multiple templatic views with minimal guidance.

Figure 1: Visualization of our method. Given an input document, we prompt the LLM to generate a Structure Unified Representation (SURe). We can use the SURe to prompt the model to generate a templatic view of the input document.

The SURe leverages LLMs to extract and present input document data in a structured manner, which can then be utilized to produce various document templates such as slides, posters, and blogs without requiring explicit supervision. This strategy is complemented by a template-agnostic evaluation method that measures the performance of generated documents across diverse formats.

SURe Generation

The SURe is defined as a JSON-based structured representation. The LLM is tasked with generating this representation through a specific prompt. This prompt instructs the model to identify and extract key sections and sentences from the input document, creating a distilled summary of the content suitable for various applications.

Figure 2: Template provided to generate the SURe.

This process requires no prior training data, demonstrating the model's potential to operate in a zero-shot learning environment. By representing the document's content in a way that highlights its most salient points, the SURe optimally prepares the information for transformation into different templatic views.

Templatic View Generation

Once a SURe is generated, it is used to guide the generation of templatic views in LaTeX format, incorporating specific style parameters for each document type. These parameters define the desired output's style—ranging from the structure of slide decks and posters to the readability of blog posts, allowing the system to adjust to varying requirements flexibly.

Unified Evaluation Metric

Recognizing the inadequacy of existing evaluation metrics, the paper introduces Template-Agnostic Evaluation (TAE) to address discrepancies in structure and organization across different document types. This framework provides a holistic scoring system that accounts for quality, order, and length across panels within documents.

Figure 3: Example of the process of obtaining the rankings for the precision ordering penalty. This process is reflexive, and panels not accounted for in the precision ordering penalty are accounted for in the recall ordering penalty.

By employing components such as ROUGE and BERTScore within this unified framework, TAE measures how closely generated documents align with human judgment.

Human Evaluation and Results

In empirical evaluations, the proposed method demonstrated improvements over baseline models, particularly in simpler LLMs such as GPT-3.5. Human evaluation further supported these findings, with documents generated using the SURe preferred 82% of the time due to superior content presentation, as evidenced by better formatting and information clarity.

Figure 4: Reasons annotators preferred each document. While annotators largely preferred documents generated with a SURe, the most common reasons for preference are better formatting and information content.

Conclusion

This research advances the field of automated document generation by presenting a unified, LLM-powered method for creating and evaluating templatic document views. It effectively bridges the gap between various document formats and provides a consistent framework for assessing generated content. Though evaluated in the scientific domain, the method's adaptability suggests potential applications across diverse fields and document types. Continuous exploration could lead to the incorporation of multi-modal content, broadening the scope and impact of AI-assisted document generation systems.