Despite their powerful capabilities, LLMs like GPT-4 face challenges in producing complex, structured tabular data. A new study titled Struc-Bench evaluates these models’ proficiency across different formats including text tables, HTML, and LaTeX, leveraging a novel fine-tuning method tailored for structured data.

Key Points:

• Performance Assessment: Struc-Bench includes models like GPT-NeoX-20B, GPT-3.5, GPT-4, and Vicuna, assessed using new metrics like P-Score and H-Score.
• Structure-Aware Fine-Tuning: The study introduces FormatCoT, a technique generating format-specific instructions to enhance model performance.
• Error Analysis: An in-depth look at areas needing improvement across six dimensions: coverage, formatting, reasoning, comprehension, pragmatics, and hallucination.

This innovative approach not only enhances LLMs’ performance in generating structured data but also opens pathways for significant advancements in AI capabilities in handling complex data patterns. Explore the full study and its implications for future AI research here.