Summary of Aligned at the Start: Conceptual Groupings in Llm Embeddings, by Mehrdad Khatir et al.
Aligned at the Start: Conceptual Groupings in LLM Embeddings
by Mehrdad Khatir, Sanchit Kabra, Chandan K. Reddy
First submitted to arxiv on: 8 Jun 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 This paper delves into the often-overlooked input embeddings of transformer-based language models (LLMs), employing fuzzy graph, k-nearest neighbor (k-NN), and community detection techniques. The analysis reveals significant categorical community structures aligned with predefined concepts and categories, mirroring human understanding. These groupings exhibit within-cluster organization, such as hierarchies or topological ordering, suggesting a fundamental structure preceding contextual processing. To investigate the conceptual nature of these groupings, cross-model alignments are explored across different LLM categories within their input embeddings, showing medium to high degrees of alignment. The paper also provides evidence that manipulating these groupings can play a functional role in mitigating ethnicity bias in LLM tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at the way computers start understanding words and ideas when we feed them information. They used special tools to study how different language models do this, finding patterns that match what humans understand. These patterns show that similar concepts are grouped together in a hierarchical way, which happens before the computer processes the meaning of the text. The researchers also found that they can use these patterns to help reduce bias in computers’ understanding of ethnicity-related topics. |
Keywords
» Artificial intelligence » Alignment » Nearest neighbor » Transformer
