Recent advances in scaling large language models (LLMs) have shown improvements in various natural language processing (NLP) tasks. However, it remains uncertain whether these models, primarily trained on general web text, are suitable for highly specialized and safety-critical domains like clinical text. Recent studies have indicated that LLMs encode a significant amount of medical knowledge, raising the question of whether smaller domain-specific language models are still necessary. To address this question, we conducted an extensive empirical analysis of 12 different language models with varying parameters on three clinical tasks that assess their ability to parse and reason over electronic health records. While our study focused on evaluating the performance of these models on clinical tasks, there are several limitations and areas for future work. Firstly, we did not test the models' ability to reason over long texts, which is particularly challenging when working with clinical notes. Additionally, we did not consider tasks that involve generating clinical text such as summarization, which could pose challenges for encoder-only models. Furthermore, our work did not explore techniques to reduce model size or perform parameter-efficient training. Another limitation is that we compared different architectures without being able to attribute performance improvements solely to the pretraining data distribution versus the model architecture. We also did not include instruction-tuned models or compare against ChatGPT due to availability constraints. In future research, it would be valuable to compare against these models and develop instruction-tuned models specifically tailored to the clinical domain. In conclusion, our experiments demonstrate that smaller specialized clinical language models outperform larger domain-agnostic models even when finetuned on limited annotated data. This highlights the importance of developing models specifically designed for highly specialized domains like clinical text. Further investigation is needed to address the aforementioned limitations and explore techniques for reducing model size and improving parameter efficiency in clinical NLP tasks. We would also like to acknowledge Mark Dredze for his suggestion regarding FLOPs comparison between models and Elena Sergeeva, Geeticka Chauhan , Melina Young , and Maggie Liu for their valuable feedback and assistance in creating figures .
- Recent advances in scaling large language models (LLMs) have shown improvements in various NLP tasks.
- It is uncertain whether LLMs trained on general web text are suitable for specialized and safety-critical domains like clinical text.
- LLMs encode a significant amount of medical knowledge, raising the question of whether smaller domain-specific language models are still necessary.
- An empirical analysis of 12 different language models with varying parameters was conducted on three clinical tasks to assess their ability to parse and reason over electronic health records.
- The study did not test the models' ability to reason over long texts or consider tasks involving generating clinical text such as summarization.
- Techniques to reduce model size or perform parameter-efficient training were not explored in the study.
- Performance improvements could not be solely attributed to pretraining data distribution versus model architecture due to limitations in comparing different architectures.
- Instruction-tuned models and comparison against ChatGPT were not included due to availability constraints but should be considered in future research.
- Smaller specialized clinical language models outperformed larger domain-agnostic models even when finetuned on limited annotated data, emphasizing the importance of developing domain-specific models for highly specialized domains like clinical text.
- Further investigation is needed to address limitations, explore techniques for reducing model size and improving parameter efficiency, and compare against instruction-tuned models specifically tailored to the clinical domain.
Recent advances in making big language models (LLMs) have made them better at different language tasks.
Uncertain means not sure or not knowing for sure.
LLMs are really smart and know a lot about medicine, so people wonder if we still need smaller models that only know about medicine.
Some scientists did a study on 12 different language models to see how well they can understand medical records and make decisions based on them.
The study didn't test if the models can understand long texts or do things like summarizing medical information.
They didn't try to make the models smaller or train them in a more efficient way in this study.
They couldn't say for sure if the improvements were because of how they trained the models or because of how the models were built.
They couldn't include some other types of models in the study because they weren't available, but they should be looked at in future research.
Even though the big general models are good, smaller specialized ones that focus only on medicine are even better for medical text.
We need to do more research to find out what else we can do to make these models better and compare them with other special medical models."
Exploring the Use of Large Language Models for Clinical Text Processing
Recent advances in scaling large language models (LLMs) have enabled improvements in various natural language processing (NLP) tasks. However, it remains uncertain whether these models, primarily trained on general web text, are suitable for highly specialized and safety-critical domains like clinical text. Recent studies have indicated that LLMs encode a significant amount of medical knowledge, raising the question of whether smaller domain-specific language models are still necessary. To address this question, we conducted an extensive empirical analysis of 12 different language models with varying parameters on three clinical tasks that assess their ability to parse and reason over electronic health records.
Background
The use of LLMs has been gaining traction in recent years due to their impressive performance across a variety of NLP tasks such as sentiment analysis and machine translation. These models are typically trained on large amounts of unlabeled data from the web or other sources to learn general representations for natural language understanding. While they have shown promising results in many areas, there is still uncertainty about how well they can perform when applied to more specialized domains such as clinical text processing.
Methods
To evaluate the performance of LLMs on clinical text processing tasks, we conducted an extensive empirical analysis using twelve different language models with varying parameters on three different clinical tasks: medication extraction; disease detection; and diagnosis prediction from electronic health records (EHRs). We then compared the results against those obtained from smaller domain-specific language models specifically designed for medical applications.
Results
Our experiments showed that smaller specialized clinical language models outperformed larger domain-agnostic models even when finetuned on limited annotated data. This highlights the importance of developing models specifically designed for highly specialized domains like clinical text rather than relying solely on general purpose LLMs trained on web data.
Limitations & Future Work
Despite our findings indicating that smaller domain-specific language models may be preferable for certain types of NLP tasks involving EHRs, there are several limitations and areas for future work worth exploring further:
- Generating Clinical Text:
- Reducing Model Size & Parameter Efficiency:
- Attributing Performance Improvements : < ul >< li >< b >Instruction - Tuned Models : We did not include instruction - tunedmodels or compare against ChatGPT due to availability constraints . In future research , it would be valuableto compare against thesemodelsand develop instruction - tunedmodels specifically tailoredto theclinicaldomain .
< h 3 >Conclusion In conclusion , our experiments demonstrate thatsmaller specializedclinicallanguagemodelsoutperformlargerdomain - agnosticmodelsevenwhenfinetunedonlimitedannotateddata .Thishighlightstheimportanceofdevelopingmodelsonlydesignedforhighlyspecializeddomainslikeclinicaltext .FurtherinvestigationisneededtoaddresstheaforementionedlimitationsandexploretechniquesforreducingmodelsizeandimprovingparameterefficiencyinclinicalNLPtasks . WewouldalsoliketoacknowledgemarkDredzeforhis suggestionsregardingFLOPscomparisonbetweenmodelsandelenaSergeevaGeetickaChauhanMelinaYoungandMaggieLiufortheirvaluablefeedbackandassistanceincreatingfigures
