Summary of Structext-eval: Evaluating Large Language Model’s Reasoning Ability in Structure-rich Text, by Zhouhong Gu et al.
StrucText-Eval: Evaluating Large Language Model’s Reasoning Ability in Structure-Rich Text
by Zhouhong Gu, Haoning Ye, Xingzhou Chen, Zeyang Zhou, Hongwei Feng, Yanghua Xiao
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary As machine learning educators, we can summarize this research paper as follows: The rapid advancement of large language models (LLMs) has led to a shift in corporate data strategies towards utilizing unstructured information. This study investigates whether LLMs can interpret structured data directly in its unstructured form without any preprocessing or conversion. To tackle this challenge, the researchers propose an automatic evaluation data generation method that generates data with adjustable complexity through controllable nesting and structural width, supporting 8 structured languages and 29 tasks. The proposed StrucText-Eval benchmark contains 5,800 pre-generated and annotated samples designed to evaluate how well LLMs understand and reason through structured text. Experimental results show that while open-source LLMs achieve a maximum accuracy of 74.9% on the standard dataset, their performance drops significantly to 45.8% on the harder dataset. In contrast, human participants reach an accuracy of 92.6% on StrucText-Eval-Hard, highlighting LLMs’ current limitations in handling intricate structural information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about how large language models (LLMs) can understand and work with structured data like tables or forms. Right now, LLMs are great at working with unstructured text like articles or emails, but they struggle to handle structured data directly. To help figure this out, the researchers created a special dataset called StrucText-Eval that contains lots of examples of structured data in different formats. They used this dataset to test how well LLMs can do tasks like summarizing tables or filling out forms correctly. The results show that while LLMs are good at some things, they’re not as good as humans when it comes to handling complex structured data. |
Keywords
» Artificial intelligence » Machine learning
