Improving Large Language Models with Graph-Structured Data
In recent years, there have been significant advancements in Large Language Model (LLM) research and applications. These generative models have captured the attention of the artificial intelligence community and have been used for various tasks. However, there are still some limitations and drawbacks to LLMs that need to be addressed.
The Limitations of LLMs
One major drawback of LLMs is their reliance on unstructured text. This can sometimes lead to the models missing logical inferences or generating false conclusions. Additionally, LLMs have limitations based on the time period they were trained and may struggle to incorporate new knowledge about how the world has evolved. Graph-structured data offers a potential solution to these limitations but has not been thoroughly explored in conjunction with LLMs.
The Intersection of Graphs and LLMs
While there has been considerable research on graph databases and LLMs, there is still a need for more exploration of the applications of graph-structured data. To address this, Wang et al. have created a graph benchmarking challenge specifically for language models. However, there are still unanswered questions and concerns surrounding this intersection.
One recent study conducted by researchers from Google Research examined reasoning over graph-structured data using LLMs. They focused on two main areas: graph prompt engineering and graph encoding. By breaking down the issue and exploring these two aspects, they aimed to uncover insights and best practices for incorporating graphs into LLMs.
Using Graphs with LLMs
One way to leverage LLM’s representations in graph problems is by experimenting with different graph encoding techniques. Researchers can also explore different prompt engineering methods to ask LLMs the questions they want answered. To evaluate the performance of LLM reasoning on graph data, the researchers created a new set of benchmarks called GraphQA. This benchmark includes diverse and realistic graph structures, setting it apart from previous studies that used LLMs.
Overall, this research contributes to a better understanding of how graphs can enhance LLMs. It provides insights into graph-structure prompting approaches, best practices for graph encoding, and a new benchmark for evaluating LLM reasoning on graph data.
For more information on this research, you can refer to the paper. All credit goes to the researchers involved in this project. Don’t forget to join our ML subreddit, Facebook community, Discord channel, and subscribe to our email newsletter to stay updated with the latest AI research news and projects.