Summary of Variational Language Concepts For Interpreting Foundation Language Models, by Hengyi Wang et al.
Variational Language Concepts for Interpreting Foundation Language Models
by Hengyi Wang, Shiwei Tan, Zhiqing Hong, Desheng Zhang, Hao Wang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a new framework called VAriational Language Concept (VALC) to provide concept-level interpretations of Foundation Language Models (FLMs), such as BERT, which are currently interpreted using attention weights. The authors formally define conceptual interpretation and demonstrate that VALC finds the optimal language concepts for FLM predictions on several real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making it easier to understand how AI models like BERT work by finding more meaningful ways to explain their decisions. Right now, people use something called attention weights to try to figure out why these models make certain predictions, but this approach only works at the level of individual words and doesn’t capture bigger ideas or concepts. The new method proposed in this paper, VALC, is designed to go beyond just looking at individual words and provide a more intuitive understanding of how these AI models work. |
Keywords
» Artificial intelligence » Attention » Bert
