Summary of Targeted Visualization Of the Backbone Of Encoder Llms, by Isaac Roberts et al.
Targeted Visualization of the Backbone of Encoder LLMs
by Isaac Roberts, Alexander Schulz, Luca Hermes, Barbara Hammer
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 explores the potential risks associated with state-of-the-art Large Language Models (LLMs) used in natural language processing (NLP). Specifically, it focuses on encoder models like BERT and GPT, which despite their success, are susceptible to biases and adversarial attacks. The authors highlight the need for explainable AI techniques to detect these issues, as current methods only provide local explanations for single inputs. Instead, they propose global methods based on dimensionality reduction for classification inspection, drawing from other domains like t-SNE in embedding space. The paper aims to contribute to a deeper understanding of NLP models and their potential biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how language processing models can be unfair or vulnerable to attacks. Even though these models are very good at what they do, they have some major problems, like being biased against certain groups or easily tricked by fake data. The authors want to make AI more transparent and trustworthy. They’re suggesting new ways to analyze complex data and spot potential issues before they cause harm. |
Keywords
* Artificial intelligence * Bert * Classification * Dimensionality reduction * Embedding space * Encoder * Gpt * Natural language processing * Nlp
