Summary of Cypherbench: Towards Precise Retrieval Over Full-scale Modern Knowledge Graphs in the Llm Era, by Yanlin Feng et al.
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
by Yanlin Feng, Simone Papicchio, Sajjadur Rahman
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 tackles the challenge of retrieving information from modern encyclopedic knowledge graphs like Wikidata to augment large language models (LLMs). The authors analyze the root cause of the inefficiency in LLMs’ ability to retrieve information from these graphs and suggest that it is due to overly large schemas, resource identifiers, overlapping relation types, and lack of normalization. To address this issue, they propose property graph views on top of the underlying RDF graph that can be efficiently queried by LLMs using Cypher. The authors instantiate this idea on Wikidata and introduce CypherBench, a benchmark with 11 large-scale, multi-domain property graphs, over 7.8 million entities, and over 10,000 questions. They also develop an RDF-to-property graph conversion engine, create a systematic pipeline for text-to-Cypher task generation, and design new evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to help big language models learn from special kinds of data called knowledge graphs. These graphs have lots of information about people, places, and things. The problem is that these models can’t easily find the information they need in these graphs because the way the data is organized makes it hard for them to understand. To solve this, the authors create a new way to look at the data that makes it easier for language models to find what they’re looking for. They test their idea on Wikidata and make a special benchmark to measure how well it works. |
