Key Points
1. Large Language Models (LLMs) like GPT-4 struggle with generating complex, structured data, and a structure-aware fine-tuning approach is proposed to improve this ability.
2. The study introduces S TRUC -B ENCH to evaluate LLMs on generating raw text, HTML, and LaTeX tables, identifying common formatting errors and potential improvements.
3. The research examines the lack of systematic analysis of LLMs' ability to produce complex structured data, the need for comprehensive benchmarks, and the potential for enhancing LLMs' performance in generating accurate format and error-free content.
4. The study contributed by developing a benchmark focusing on generating structured texts in various formats, conducting empirical evaluations of popular LLMs, and suggesting structure-aware fine-tuning methods to enhance LLMs' ability to generate structured outputs.
5. The limitations of GPT-3.5 and GPT-4 in handling complex structured output are identified, with error analysis and annotations highlighting specific error types.
6. The study introduces F ORMAT C OT to generate data-instruction pairs and proposes a structure-aware instruction tuning method to bolster LLMs' capability in generating structured text.
7. Evaluation metrics for tables' content and structure similarity are proposed, with comparisons between model-based and hand-crafted scoring functions to rate similarity.
8. Comparative analysis of language models on performance metrics and human evaluations demonstrate the efficacy of the proposed metrics in assessing the format consistency and content consistency of generated examples.
9. The research concludes with insights, limitations, and future directions for exploring advanced methods and multimodal LLMs to further improve their abilities in generating structured data.
Summary
The research paper assesses the capability of Large Language Models (LLMs) in generating complex, structured outputs and proposes a structure-aware fine-tuning approach to improve this ability. The study introduces the S TRUC -B ENCH benchmark, evaluates five representative LLMs on raw text, HTML, and LaTeX tables, and identifies common formatting errors and areas for improvement. The research presents an ability map of LLM capabilities and suggests promising directions for future work. It highlights significant advancements made in natural language processing tasks by LLMs but emphasizes their underperformance in generating complex structured outputs.
Analysis of LLMs' Performance
The paper addresses a lack of systematic analysis, fine-grained evaluation, and comprehensive benchmarks of LLMs' performance in generating structured outputs. It introduces S TRUC -B ENCH focusing on structured texts in raw text, HTML, and LaTeX formats, uncovering key issues in content accuracy, formatting, numerical reasoning, and handling long tables. The study also proposes a structure-aware instruction tuning method, which significantly improves the LLMs' ability to generate structured outputs.
Limitations and Future Directions
The paper presents a notable limitation of specific LLMs in handling complex structured outputs, particularly in tasks like text-to-table conversion. It offers an in-depth analysis of the shortcomings observed and proposes an ability map of LLM capabilities from six dimensions. The findings suggest the need for domain-specific benchmarks, a broader variety of datasets, enhanced numerical reasoning capabilities, and the exploration of multimodal LLMs for structured text generation. Despite the comprehensive analysis provided, the paper acknowledges the limitations and offers directions for future research and advancements in the field.
Reference: https://arxiv.org/abs/2309.08963