Summary of From Understanding to Utilization: a Survey on Explainability For Large Language Models, by Haoyan Luo et al.
From Understanding to Utilization: A Survey on Explainability for Large Language Models
by Haoyan Luo, Lucia Specia
First submitted to arxiv on: 23 Jan 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 highlights the importance of explainability in Large Language Models (LLMs), particularly Transformer-based models like the LLaMA family. As these models become integral to various applications, their lack of transparency raises concerns about ethics and usability. The survey explores existing methods for explaining LLMs, categorized as local and global analyses, and evaluates their advantages and limitations. Additionally, it examines representative evaluation metrics and datasets, proposing exciting avenues for explanatory techniques and their applications in the LLM era. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like language. But because they’re so powerful, we need to make sure we understand how they work. This paper talks about making LLMs more transparent, or “explainable”. It looks at different ways to do this, like editing the model or generating new text based on what it learned. The goal is to use these explainable models in a way that’s fair and safe. |
Keywords
* Artificial intelligence * Llama * Transformer
