Summary of Alpapico: Extraction Of Pico Frames From Clinical Trial Documents Using Llms, by Madhusudan Ghosh et al.
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
by Madhusudan Ghosh, Shrimon Mukherjee, Asmit Ganguly, Partha Basuchowdhuri, Sudip Kumar Naskar, Debasis Ganguly
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); 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 paper proposes a novel approach to automatically extracting Population, Intervention, Comparator, and Outcome (PICO) frames from clinical trial studies. The existing methods for PICO frame extraction rely on supervised learning with manually annotated data points. In contrast, this work employs In-Context Learning (ICL) strategy using pre-trained Large Language Models (LLMs) to extract PICO-related terminologies in an unsupervised setup. To further improve the performance of LLMs, the authors adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA). The proposed framework is evaluated on various EBM-NLP datasets and achieves state-of-the-art results. This approach has the potential to alleviate the time-consuming process of manually scrutinizing systematic reviews. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier for computers to understand clinical trial reports. Right now, it takes a lot of time to read through all these reports and find important information. The researchers want to use special language models to automatically extract this information. They’re trying a new way called In-Context Learning that doesn’t need labeled data points. They also tried another technique called Low Rank Adaptation to make the model even better. The results show that their approach works really well on different datasets. This could be very helpful for people who need to analyze lots of clinical trial reports. |
Keywords
» Artificial intelligence » Instruction tuning » Lora » Low rank adaptation » Nlp » Supervised » Unsupervised
