Summary of A General Framework For Producing Interpretable Semantic Text Embeddings, by Yiqun Sun et al.
A General Framework for Producing Interpretable Semantic Text Embeddings
by Yiqun Sun, Qiang Huang, Yixuan Tang, Anthony K. H. Tung, Jun Yu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 This paper introduces CQG-MBQA, a framework for generating interpretable semantic text embeddings for diverse Natural Language Processing (NLP) tasks. Unlike black-box models that rely on expert input or well-prompt design, CQG-MBQA uses contrastive question generation and multi-task binary question answering to produce discriminative yes/no questions and answers. The approach yields high-quality embeddings comparable to advanced black-box models while maintaining interpretability. Extensive experiments demonstrate the effectiveness of CQG-MBQA across various tasks, outperforming other interpretable text embedding methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to understand how computers process and analyze text. Right now, there are powerful computer models that can do this well, but they’re hard to understand. The researchers created a new way to make these models more transparent by asking yes/no questions about the text. This makes it easier for humans to understand what the computer is doing. They tested their approach and found that it works just as well as some of the best computer models, while still being easy to understand. |
Keywords
» Artificial intelligence » Embedding » Multi task » Natural language processing » Nlp » Prompt » Question answering
