Michael Alexander Riegler’s Post

The adoption of LLMs in healthcare is quick, but several important questions remain. One we addressed in our latest work is: are we paying enough attention to the subtle choices that determine their reliability?  While the focus is often on the model itself, a seemingly minor technical detail, how the model chooses its next word, can alter the quality of its output. In "A Comparative Study of Decoding Strategies in Medical Text Generation" led by Oriana Presacan, we systematically investigated how 11 different decoding strategies impact the performance of both general-purpose and medically-specialized LLMs across five distinct healthcare tasks. Full paper: https://lnkd.in/df9AjssD Code and dataset: https://lnkd.in/dF5kuuHb Our findings challenge some common assumptions about deploying LLMs in high-stakes environments: -The decoding strategy is not a minor detail; it is an important choice. We found that deterministic strategies like beam search consistently produce higher quality and more reliable outputs than common stochastic methods like top-k sampling. The choice of strategy can be as significant as the choice of the LLM itself. -Specialized medical LLMs are surprisingly sensitive. Contrary to the belief and discussions that domain-specific models are inherently more robust, our results show they are significantly more sensitive to the selected decoding method than their general-purpose counterparts. A medical model that performs well with one strategy can see its performance degrade significantly with another. -There is also a direct trade-off between speed and quality. Our analysis revealed a statistically significant positive correlation between inference time and output quality. In medical applications where accuracy is connected to safety and therefore most important, prioritizing speed by using faster, less deliberate decoding methods can directly compromise the reliability of the generated text. So what does this mean for people working with LLMs in healthcare? In my opinion, we need to shift our focus from just which model to use, to how we use it. This requires that we are recognizing that there is no one-size-fits-all solution and that decoding strategies must be evaluated and thought about for each specific medical use case. Second, treating specialized models not as a simple solution but as powerful tools that require even more careful tuning and testing to ensure their stability. And finally, deliberately prioritizing quality and consistency over raw inference speed in clinical applications (until we get models that can do both). with Oriana Presacan, Alireza Nik, Vajira Thambawita and Bogdan Ionescu Simula Research Laboratory Simula Metropolitan Center for Digital Engineering (SimulaMet) CAMPUS Research Institute University POLITEHNICA of Bucharest OsloMet – Oslo Metropolitan University #AI #LLM #Decoding #Healthcare