Summary of Kalm: Knowledge-aligned Autoregressive Language Modeling Via Dual-view Knowledge Graph Contrastive Learning, by Peng Yu et al.
KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning
by Peng Yu, Cheng Deng, Beiya Dai, Xinbing Wang, Ying Wen
First submitted to arxiv on: 6 Dec 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 The paper proposes KaLM, a Knowledge-aligned Language Modeling approach that fine-tunes autoregressive Large Language Models (LLMs) to align with high-quality structured knowledge bases, such as knowledge graphs (KGs). LLMs are inherently proficient in generative tasks but struggle with knowledge-driven tasks like factual knowledge querying. By leveraging KGs, KaLM aims to compensate for the knowledge deficiencies of LLMs while preserving their generative capabilities. The approach involves two objectives: explicit knowledge alignment through dual-view knowledge graph contrastive learning and implicit knowledge alignment through triple completion language modeling. This yields a significant performance boost in evaluations of knowledge-driven tasks such as embedding-based KG completion and generation-based KG question answering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are really good at generating text, but they’re not so great when it comes to knowing facts. That’s because they were trained on lots of text data, which can be biased or incomplete. To help LLMs do better with factual knowledge, researchers use something called a “knowledge graph” (KG). A KG is like an encyclopedia that has all the information organized in a special way. The new approach, called KaLM, tries to teach LLMs how to work with KGs by fine-tuning them on KG data. This makes the LLMs better at understanding and generating text about facts. It’s like giving them a superpower! |
Keywords
» Artificial intelligence » Alignment » Autoregressive » Embedding » Fine tuning » Knowledge graph » Question answering
