Summary of Graphtranslator: Aligning Graph Model to Large Language Model For Open-ended Tasks, by Mengmei Zhang et al.
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
by Mengmei Zhang, Mingwei Sun, Peng Wang, Shen Fan, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Cheng Yang, Chuan Shi
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed GraphTranslator bridges the gap between large language models (LLMs) and graph models (GMs), enabling effective handling of both pre-defined and open-ended tasks in the graph domain. By leveraging GMs for predefined tasks and LLMs for open-ended ones, this Translator empowers predictions based on language instructions. The approach involves training a Producer to construct graph-text alignment data and translating node representations into tokens. This unified perspective is demonstrated through extensive results showcasing zero-shot node classification and graph question answering experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like ChatGPT have revolutionized various fields by exhibiting powerful capabilities in open-ended tasks. However, these models are restricted to pre-defined forms when applied to the graph domain. A new approach called GraphTranslator bridges this gap by leveraging graph models for predefined tasks and large language models for open-ended ones. This allows predictions based on language instructions, providing a unified perspective. |
Keywords
» Artificial intelligence » Alignment » Classification » Question answering » Zero shot
