Summary of Unveiling Llms: the Evolution Of Latent Representations in a Dynamic Knowledge Graph, by Marco Bronzini et al.
Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph
by Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 end-to-end framework decodes factual knowledge embedded in token representations from a vector space to a set of ground predicates, showcasing layer-wise evolution using a dynamic knowledge graph. Activation patching, a vector-level technique, is employed to extract encoded knowledge without relying on training or external models. Interpretability analyses at local and global levels demonstrate entity centrality in LLM reasoning, representation errors causing erroneous evaluation, and trends in the underlying evolution from word-based knowledge to claim-related facts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how large language models (LLMs) work by decoding the factual information they represent internally. The authors developed a special framework that can extract this knowledge without needing training or extra help. They used two datasets of claims and showed that their approach can give insights into how LLMs reason at different levels. This research can improve our understanding of how LLMs make decisions and use language. |
Keywords
» Artificial intelligence » Knowledge graph » Token » Vector space
