Summary of Challenges and Opportunities in Text Generation Explainability, by Kenza Amara et al.
Challenges and Opportunities in Text Generation Explainability
by Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
First submitted to arxiv on: 14 May 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 This research paper proposes a framework for developing attribution-based explainable artificial intelligence (xAI) methods tailored to natural language processing (NLP) tasks, particularly text generation. The authors identify 17 challenges that arise during the development and assessment of these methods, categorized into three groups: tokenization, explanation similarity and importance, and human intervention. These challenges include issues such as defining explanation similarity, determining token importance and prediction change metrics, and creating suitable test datasets. The paper highlights the need for a deeper understanding of text generation and proposes new opportunities for the NLP community to develop probabilistic word-level explainability methods and engage humans in the explainability pipeline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make large language models more understandable by developing special tools called attribution-based explainable artificial intelligence (xAI) methods. These methods help us understand why a model makes certain predictions or generates specific text. The researchers found 17 challenges when trying to develop and test these methods, like deciding what makes one explanation better than another, figuring out which words are most important in the generated text, and getting humans involved in the process. This work is important for understanding how language models work and can help us make them more useful. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Text generation » Token » Tokenization
