Summary of Struct-x: Enhancing Large Language Models Reasoning with Structured Data, by Xiaoyu Tan et al.
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
by Xiaoyu Tan, Haoyu Wang, Xihe Qiu, Yuan Cheng, Yinghui Xu, Wei Chu, Yuan Qi
First submitted to arxiv on: 17 Jul 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 This paper proposes a novel framework called Struct-X to efficiently integrate structured data into large language models (LLMs), enhancing their reasoning abilities. The framework operates through five phases: “read-model-fill-reflect-reason”, which encodes structured data, fills in missing entity information, filters out irrelevant tokens, constructs a topological network, and generates prompts for LLM inference. Struct-X is evaluated on benchmarks such as the knowledge graph question-answer task and the long document reading comprehension task, showing significant improvements in LLM reasoning with complex input context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand structured data better by using a new way to combine this type of data with large language models. The new method is called Struct-X and it has five steps: read, model, fill, reflect, and reason. This makes the computer program (LLM) smarter and more able to understand complex information. |
Keywords
» Artificial intelligence » Inference » Knowledge graph
