Evaluating Large Language Models' Ability to Generate Complex Structured Tabular Data

Core Concepts

Large Language Models (LLMs) struggle to generate complex structured tabular data, despite their advanced text generation capabilities. This study introduces a comprehensive benchmark, STRUC-BENCH, to assess LLMs' performance on this task and proposes a structure-aware fine-tuning method to improve their abilities.

Abstract

The study examines the challenges faced by Large Language Models (LLMs) in generating complex structured tabular data, which is an essential skill for practical applications like coding copilots and automated report generation.
The key highlights are:

Lack of systematic analysis and comprehensive benchmarks to evaluate LLMs' ability to output complex structured tabular data. Previous efforts have focused on simple Information Extraction (IE) tasks rather than structured data generation.

Lack of evaluation metrics that consider both the content and format of the generated tables. Existing benchmarks rely on rudimentary metrics like word overlap, which may be insufficient for evaluating structured output.

Lack of methods to enhance the performance of current LLMs to better follow natural language inputs and generate tabular outputs with the correct format.

The study introduces STRUC-BENCH, a benchmark specifically constructed for generating structured tabular data, covering text tables, HTML, and LaTeX formats. It also proposes two innovative metrics, P-Score and H-Score, to more accurately gauge LLM performance on this task.
Furthermore, the study presents a structure-aware fine-tuning method, FORMATCOT, which utilizes GPT-3.5 to generate format instructions and then fine-tunes the LLaMA-7B model to follow these formats. The results demonstrate that with FORMATCOT, smaller models can outperform larger models in this specific task.
The study also provides an in-depth error analysis and creates an ability map across six dimensions - coverage, formatting, reasoning, comprehension, pragmatics, and hallucination - to highlight areas for future enhancements and suggest forthcoming research trajectories.

Stats

The Grizzlies shot 50 percent from the field.
The Grizzlies outscored the Suns 30-19 in the third quarter and 26-20 in the final period.
The Grizzlies out-rebounded Phoenix 37-35 and outscored the Suns in the paint 46-32.
The Grizzlies registered 25 assists compared to only 13 for the Suns.
Courtney Lee scored 22 points (9-14 FG, 4-5 3Pt).
Mike Conley led all scorers with 24 points (9-14 FG, 3-4 3Pt) and 11 assists.
Marc Gasol added 18 points, six assists, and five rebounds.
Eric Bledsoe scored 23 points (9-12 FG) with five rebounds and four assists.
Goran Dragic scored just six points in 26 minutes.
Isaiah Thomas had 15 points and two assists off the bench.
Markieff Morris added 20 points and five rebounds.

Quotes

"The Grizzlies (50) used a strong second half to outlast the Suns (3 - 2) 102 - 91 in Phoenix on Wednesday night."
"Memphis found itself behind six at halftime but outscored Phoenix 30 - 19 in the third quarter and 26 - 20 in the final period."
"The Grizzlies shot 50 percent from the field, led by strong performances from Courtney Lee and Mike Conley."

Key Insights Distilled From

Struc-Bench

by Xiangru Tang... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2309.08963.pdf

Deeper Inquiries

What other data formats or representations could be explored to further enhance the structured data generation capabilities of LLMs?

To further enhance the structured data generation capabilities of LLMs, exploring additional data formats and representations could be beneficial. Some potential formats to consider include:

Graph Data: LLMs could be trained to generate structured data in the form of graphs, which are commonly used to represent relationships between entities.
Temporal Data: Incorporating temporal data formats could enable LLMs to generate structured data that includes time-based information, such as time series data or event sequences.
Spatial Data: Including spatial data formats could allow LLMs to generate structured data related to geographical locations, maps, or spatial relationships.
Multimodal Data: Combining text with other modalities like images, audio, or video could lead to the generation of more comprehensive and rich structured data representations.

How could the proposed structure-aware fine-tuning approach be extended to handle more complex or domain-specific table structures?

The proposed structure-aware fine-tuning approach can be extended to handle more complex or domain-specific table structures by:

Customized Instruction Generation: Tailoring the instruction generation process to specific domain requirements, ensuring that the generated instructions are relevant and detailed for the given domain.
Domain-Specific Training Data: Curating training data that is specific to the domain of interest, including a diverse range of examples that cover the complexities and nuances of the domain-specific table structures.
Fine-Tuning with Domain-Specific Data: Fine-tuning the LLMs on domain-specific data using the generated format instructions, allowing the models to learn the intricacies of the domain-specific table structures.
Iterative Feedback Loop: Implementing an iterative feedback loop where the model's performance on domain-specific structures is evaluated, and the fine-tuning process is refined based on the feedback received.

What other factors, beyond content and format, could be considered to develop a more comprehensive evaluation framework for structured data generation by LLMs?

In addition to content and format, several other factors could be considered to develop a more comprehensive evaluation framework for structured data generation by LLMs. Some of these factors include:

Consistency: Evaluating the consistency of the generated structured data across different examples and ensuring that the information is coherent and logically connected.
Contextual Understanding: Assessing the LLMs' ability to understand the context of the data and generate structured outputs that are contextually relevant and accurate.
Error Analysis: Conducting in-depth error analysis to identify common error patterns and areas of improvement in the generated structured data.
Scalability: Evaluating the scalability of the LLMs in handling large and complex structured data sets, ensuring that the models can generate structured outputs efficiently and accurately for varying data sizes.
Interpretability: Considering the interpretability of the generated structured data, ensuring that the outputs are understandable and meaningful to end-users or downstream applications.