Summary of Give: Structured Reasoning Of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation, by Jiashu He et al.
GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation
by Jiashu He, Mingyu Derek Ma, Jinxuan Fan, Dan Roth, Wei Wang, Alejandro Ribeiro
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Existing approaches to improving the reasoning capacities of Large Language Models (LLMs) rely on their internal knowledge, but these methods have limitations. In contrast, Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that combines parametric and non-parametric memories to enhance accurate reasoning with minimal external input. GIVE guides LLM agents to select relevant expert data, engage in query-specific thinking, and synthesize information to produce the final output. The framework demonstrates significant benefits, including improved performance across various LLM sizes, surpassing larger models on scientific tasks, effective application on scientific and open-domain assessments, and ability to reason using restricted or noisy knowledge sources. GIVE also enables training-free problem-solving beyond an LLM’s training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to improve the thinking abilities of large language models. Instead of relying only on their own knowledge, this method combines different types of information to make better decisions. The approach is called Graph Inspired Veracity Extrapolation (GIVE). GIVE helps language models think more accurately and makes them able to solve problems that are too difficult for them alone. The results show that this method can improve the performance of language models and help them work with smaller or noisier sources of information. |
