Summary of Reasoning Factual Knowledge in Structured Data with Large Language Models, by Sirui Huang et al.
Reasoning Factual Knowledge in Structured Data with Large Language Models
by Sirui Huang, Yanggan Gu, Xuming Hu, Zhonghao Li, Qing Li, Guandong Xu
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have excelled in various natural language processing tasks due to their ability to comprehend and reason with factual knowledge. However, this capability is challenged when dealing with structured data, which possesses distinct characteristics differing from unstructured texts used for pretraining. To evaluate LLMs’ structural reasoning capabilities in inferring factual knowledge, we propose the StructFact benchmark, comprising 8,340 factual questions across various tasks, domains, timelines, and regions. This benchmark allows us to investigate LLMs’ performance across five factual tasks derived from structured facts. Extensive experiments on different LLMs with varying training strategies reveal current limitations in inferring factual knowledge from structured data. The proposed benchmark serves as a compass to navigate LLMs’ strengths and weaknesses in reasoning with structured data, encouraging advancements in related real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very good at answering questions using text. But when it comes to information stored in tables or databases, they struggle to make the right connections. To help them get better at this, researchers created a test called StructFact. It has 8,340 questions that cover different topics and types of information. By looking at how well language models do on these questions, we can see what they’re good at and what they need to work on. This will help make sure they’re used correctly in real-life situations. |
Keywords
» Artificial intelligence » Natural language processing » Pretraining
