Summary of Ravel: Evaluating Interpretability Methods on Disentangling Language Model Representations, by Jing Huang et al.
RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations
by Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, Atticus Geiger
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 research paper proposes a new dataset, RAVEL, to evaluate the effectiveness of various interpretability methods in disentangling the roles of individual neurons in representing high-level concepts. The authors introduce Multi-task Distributed Alignment Search (MDAS), a method that finds distributed representations satisfying multiple causal criteria, and demonstrate its state-of-the-art performance on RAVEL using the Llama2-7B language model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Ravel is a new way to study how our brains understand different ideas. Right now, scientists are trying to figure out which parts of the brain are important for understanding certain concepts. This paper makes it easier to compare different methods for doing this by creating a special dataset called RAVEL. They also came up with a new method called MDAS that helps find the patterns in our brains that let us understand things. |
Keywords
* Artificial intelligence * Alignment * Language model * Multi task
