Summary of Retrieval-augmented Generation Meets Data-driven Tabula Rasa Approach For Temporal Knowledge Graph Forecasting, by Geethan Sannidhi et al.
Retrieval-Augmented Generation Meets Data-Driven Tabula Rasa Approach for Temporal Knowledge Graph Forecasting
by Geethan Sannidhi, Sagar Srinivas Sakhinana, Venkataramana Runkana
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to temporal Knowledge Graph (tKG) forecasting is proposed in this paper, addressing challenges faced by pre-trained large language models (PLLMs) like inaccurate factual recall, hallucinations, biases, and future data leakage. The authors introduce sLA-tKGF, a small-scale language assistant that utilizes Retrieval-Augmented Generation (RAG) aided custom-trained language models to effectively forecast future events within tKGs. The framework constructs knowledge-infused prompts with historical data from tKGs, web search results, and PLLM-generated textual descriptions to understand entity relationships prior to the target time. This approach leverages these prompts for deeper understanding of context-specific semantic and temporal information, reducing hallucinations and mitigating distributional shift challenges through comprehending changing trends over time. The proposed framework enables more accurate and contextually grounded forecasts while minimizing computational demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict future events in Knowledge Graphs (KGs) that is more accurate and trustworthy than current methods. The authors use small language models that are trained from scratch, rather than relying on large pre-trained models like ChatGPT or Gemini. This approach helps solve problems with factual accuracy, hallucinations, biases, and leakage of future data. The method uses a combination of historical data from KGs, web search results, and descriptions generated by the small language model to understand how entities are related over time. This allows for more accurate predictions that take into account changing trends and patterns. |
Keywords
» Artificial intelligence » Gemini » Knowledge graph » Language model » Rag » Recall » Retrieval augmented generation
