Summary of Towards Generating Informative Textual Description For Neurons in Language Models, by Shrayani Mondal et al.
Towards Generating Informative Textual Description for Neurons in Language Models
by Shrayani Mondal, Rishabh Garodia, Arbaaz Qureshi, Taesung Lee, Youngja Park
First submitted to arxiv on: 30 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 Recent advancements in transformer-based language models have enabled them to capture a wide range of knowledge that can be adapted for various tasks with limited resources. However, it is unclear which pieces of information these models understand, and the neuron-level contributions in identifying them are largely unknown. This paper proposes a novel framework that ties textual descriptions to neurons, leveraging generative language models to discover human-interpretable descriptors present in a dataset. The proposed approach uses an unsupervised method to explain neurons with these descriptors, demonstrating its effectiveness through various qualitative and quantitative analyses. Specifically, the experiment shows that the proposed approach achieves 75% precision@2 and 50% recall@2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models work. It’s like trying to figure out what a brain neuron is responsible for by looking at what it connects to in the brain. Right now, we don’t know which parts of our knowledge these language models are using, and that’s important because we want to make them better. The researchers propose a new way to do this without needing lots of manual work. They show that their method can help us understand how the neurons in these language models work and what they’re responsible for. This is useful because it could help us make language models more accurate and useful. |
Keywords
» Artificial intelligence » Precision » Recall » Transformer » Unsupervised
