Summary of Addressing Hallucinations in Language Models with Knowledge Graph Embeddings As An Additional Modality, by Viktoriia Chekalina et al.
Addressing Hallucinations in Language Models with Knowledge Graph Embeddings as an Additional Modality
by Viktoriia Chekalina, Anton Razzhigaev, Elizaveta Goncharova, Andrey Kuznetsov
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach to reducing hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. The method involves transforming input text into KG embeddings and using an adapter to integrate these embeddings into the language model space, without relying on external retrieval processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models be more accurate by combining them with knowledge graphs. It’s like adding a new way for the model to understand what it’s reading. The method works by changing text into special representations that can be used alongside language model inputs, which helps reduce mistakes and makes the model more reliable. |
Keywords
» Artificial intelligence » Language model
