Summary of New Faithfulness-centric Interpretability Paradigms For Natural Language Processing, by Andreas Madsen
New Faithfulness-Centric Interpretability Paradigms for Natural Language Processing
by Andreas Madsen
First submitted to arxiv on: 27 Nov 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 Ph.D. thesis addresses the crucial issue of providing faithful explanations for complex general-purpose neural NLP models. The researchers develop new paradigms in interpretability by creating faithfulness metrics and applying lessons learned from this investigation to design new models. Two novel approaches are explored: Faithfulness Measurable Models (FMMs) and Self-Explanations. FMMs are designed to ensure that measuring faithfulness is cheap and precise, enabling optimization of explanations towards maximum faithfulness. The study finds that FMMs yield near-theoretical optimal faithful explanations. Interestingly, simple modifications to the model, such as randomly masking the training dataset, can drastically change the situation and result in consistently faithful explanations. The paper also investigates post-hoc and intrinsic explanations, which are typically task- and model-dependent. However, when using FMMs, even with the same post-hoc explanation methods, these limitations disappear, producing faithfully explainable models. Overall, this research provides valuable insights into ensuring faithfulness in complex neural NLP models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how a powerful computer program works. You want to know why it made certain decisions or said certain things. But right now, we don’t have good ways to explain these programs’ actions. This paper tries to change that by developing new methods for making sure these explanations are accurate and reliable. They test two different approaches: one where the program explains itself and another where the model is designed from the start to be explainable. The results show that with a little tweaking, even complex models can produce faithful explanations. This could have big implications for using artificial intelligence in areas like medicine or finance. |
Keywords
» Artificial intelligence » Nlp » Optimization
