Summary of Enhancing Robustness in Biomedical Nli Models: a Probing Approach For Clinical Trials, by Ata Mustafa
Enhancing Robustness in Biomedical NLI Models: A Probing Approach for Clinical Trials
by Ata Mustafa
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Medium Difficulty summary: Large Language Models have transformed industries such as Conversational AI, Content Generation, and Medical, among others. In medical research, they analyze clinical trials for entailment. However, these models are susceptible to shortcut learning, factual inconsistencies, and performance degradation with minimal context changes. To ensure model integrity, adversarial testing is performed. Despite this, ambiguity persists. To investigate the model’s syntactic and semantic understanding, mnestic probing was used on the Sci-five model trained on clinical trials. The results show that fine-tuning the model using iterative null projection improves its accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Large Language Models have changed many areas like medicine and computer science. They help analyze medical trial data to understand what it says. But these models can be tricked or give wrong answers when there’s little change in what they’re looking at. To fix this, experts test the models again to make sure they’re correct. Even with testing, some issues remain. To understand how well the model understands words and meanings, scientists used a special method called mnestic probing on a model trained for medical trial analysis. The results show that making small changes to the model helps it get better at understanding. |
Keywords
* Artificial intelligence * Fine tuning
