Summary of Beyond Self-consistency: Ensemble Reasoning Boosts Consistency and Accuracy Of Llms in Cancer Staging, by Chia-hsuan Chang et al.
Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging
by Chia-Hsuan Chang, Mary M. Lucas, Yeawon Lee, Christopher C. Yang, Grace Lu-Yao
First submitted to arxiv on: 19 Apr 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 Advances in large language models (LLMs) have led to their adoption in healthcare, where unstructured notes contain vital clinical information. The study focuses on extracting cancer staging status from clinical reports using natural language processing. With the emergence of clinical-oriented LLMs, it is promising to extract this status without extensive algorithm training. To improve trustworthiness, prompting approaches like chain-of-thought may be employed. Self-consistency further improves model performance but often leads to inconsistent generations across multiple reasoning paths. The study proposes an ensemble reasoning approach to enhance consistency and performance. Using an open-access clinical LLM to determine pathologic cancer stage from real-world pathology reports, the results demonstrate that the ensemble approach improves both consistency and performance in determining cancer stage, showcasing potential for reliable model applications in healthcare or other domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on using computers to understand and extract important information from doctors’ notes. These notes often contain vital details about patients, like their cancer diagnosis. The study shows how to make a computer program better at extracting this kind of information by combining different approaches. This could be useful in healthcare settings where accuracy is crucial. |
Keywords
* Artificial intelligence * Natural language processing * Prompting
