Large language models (LLMs) have gained popularity in the AI community as they are seen as a step towards artificial general intelligence (AGI). However, LLMs have limitations, such as dependence on unstructured text and difficulty integrating new knowledge. Researchers are exploring the use of graph-structured data to address these issues. Google Research has conducted investigations on reasoning over graph-structured data using LLMs, resulting in the creation of a graph benchmark called GraphQA. This research provides insights into graph-structure prompting approaches and best practices for encoding graphs as text for LLM usage.
How Encoding Graph Data Elevates Language Model Performance on Complex Tasks
Large language models (LLMs) have made significant advancements in recent years, captivating the AI community. However, there are still areas for improvement in their design and implementation. LLMs often rely on unstructured text, which can lead to logical inferences being overlooked or false conclusions being imagined.
Additionally, LLMs have limitations based on the time period they were trained, making it challenging to integrate new knowledge about the evolving world. Graph-structured data offers a potential solution to these problems, but there has been limited research at the intersection of graphs and LLMs.
Researchers have created a graph benchmarking challenge specifically for language models to address this gap. They have also investigated reasoning over graph-structured data as text that LLMs can read. By experimenting with different graph encoding techniques, LLMs can be used in graph problems. The researchers have developed benchmarks called GraphQA to evaluate LLM reasoning performance on graph data.
This research contributes to a thorough examination of graph-structure prompting approaches, best practices for encoding graphs as text, and a new graph benchmark called GraphQA.
If you want to evolve your company with AI and stay competitive, consider how encoding graph data can elevate language model performance on complex tasks. AI can redefine your way of work by automating key customer interactions, defining measurable impacts on business outcomes, selecting customized AI solutions, and implementing AI gradually.
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