Summary of Beneath the Surface Of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in Llms, by Maxim Ifergan et al.
Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs
by Maxim Ifergan, Leshem Choshen, Roee Aharoni, Idan Szpektor, Omri Abend
First submitted to arxiv on: 20 Aug 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 A novel study investigates the ability of large language models (LLMs) to represent factual knowledge across languages, shedding light on the inconsistencies in their responses. The researchers propose a methodology to measure representation sharing across languages by repurposing knowledge editing methods. Using a new multilingual dataset, they examine LLMs with various configurations and reveal that high consistency does not necessarily imply shared representation. They also find that script similarity is a dominant factor in representation sharing. The study highlights the need for improved multilingual knowledge representation in LLMs, suggesting a path for developing more robust and consistent models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers looked at how well language models can understand facts when they are written in different languages. They found that these models don’t always agree on what the fact is, even if it’s the same thing in different languages. The scientists also discovered that if a model can share its understanding of a fact between languages, it can actually get better at answering questions. This study shows us that language models need to be able to understand facts in many languages in order to be really good. |
