Summary of Beyond Label Attention: Transparency in Language Models For Automated Medical Coding Via Dictionary Learning, by John Wu et al.
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning
by John Wu, David Wu, Jimeng Sun
First submitted to arxiv on: 31 Oct 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 Medical coding is a time-consuming task in healthcare that can be improved by large language models (LLMs). However, interpretability remains crucial to maintain patient trust. Current methods rely on label attention mechanisms, but these often highlight unrelated tokens. To address this, the paper proposes dictionary learning, which extracts sparse representations from dense LLM embeddings. This approach goes beyond token-level explanations and builds an interpretable dictionary that enhances mechanistic-based explanations for ICD code predictions. Dictionary features can steer model behavior, elucidate hidden meanings of irrelevant tokens, and be human-interpretable. The method is shown to improve interpretability by highlighting medically relevant information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical coding is a big problem in healthcare because it takes a lot of time. Doctors need to translate medical notes into standardized codes, but this process can take hours. Artificial intelligence (AI) models could help make this process faster and better. However, the AI models also need to be transparent so that doctors understand why they made certain decisions. The current methods used for making these AI models transparent are not very good because they highlight irrelevant information. To solve this problem, researchers developed a new method called dictionary learning. This method helps the AI model make more accurate and transparent predictions by showing what’s important and what’s not. The new method is shown to be much better than current methods. |
Keywords
» Artificial intelligence » Attention » Token
