Boosting Healthcare LLMs Through Retrieved Context
Large Language Models (LLMs) have demonstrated remarkable capabilities in
natural language processing, and yet, their factual inaccuracies and
hallucinations limits their application, particularly in critical domains like
healthcare. Context retrieval methods, by introducing relevant information as
input, have emerged as a crucial approach for enhancing LLM factuality and
reliability. This study explores the boundaries of context retrieval methods
within the healthcare domain, optimizing their components and benchmarking
their performance against open and closed alternatives. Our findings reveal how
open LLMs, when augmented with an optimized retrieval system, can achieve
performance comparable to the biggest private solutions on established
healthcare benchmarks (multiple-choice question answering). Recognizing the
lack of realism of including the possible answers within the question (a setup
only found in medical exams), and after assessing a strong LLM performance
degradation in the absence of those options, we extend the context retrieval
system in that direction. In particular, we propose OpenMedPrompt a pipeline
that improves the generation of more reliable open-ended answers, moving this
technology closer to practical application.
66f50e15aa828de5c32d47aa
Jordi Bayarri-Planas
claimed_verified
2024-09-26T09:17:31.011Z
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/avatars/55109cf647c38499b471a05e3dd04ba3.svg
Jordi Bayarri
user
JordiBayarri
66f50e15aa828de5c32d47ab
Ashwin Kumar Gururajan
66f50e15aa828de5c32d47ac
Dario Garcia-Gasulla
extracted_confirmed
2024-09-26T07:52:49.778Z
62f7a16192950415b637e201
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Dario
user
dariog
Boosting Healthcare LLMs Through Retrieved Context
Large Language Models (LLMs) have demonstrated remarkable capabilities in
natural language processing, and yet, their factual inaccuracies and
hallucinations limits their application, particularly in critical domains like
healthcare. Context retrieval methods, by introducing relevant information as
input, have emerged as a crucial approach for enhancing LLM factuality and
reliability. This study explores the boundaries of context retrieval methods
within the healthcare domain, optimizing their components and benchmarking
their performance against open and closed alternatives. Our findings reveal how
open LLMs, when augmented with an optimized retrieval system, can achieve
performance comparable to the biggest private solutions on established
healthcare benchmarks (multiple-choice question answering). Recognizing the
lack of realism of including the possible answers within the question (a setup
only found in medical exams), and after assessing a strong LLM performance
degradation in the absence of those options, we extend the context retrieval
system in that direction. In particular, we propose OpenMedPrompt a pipeline
that improves the generation of more reliable open-ended answers, moving this
technology closer to practical application.
The study enhances the factuality and reliability of large language models in healthcare through optimized context retrieval methods, achieving performance comparable to private solutions in benchmarks and proposing OpenMedPrompt for realistic open-ended answers.
Large Language Models context retrieval healthcare domain multiple-choice question answering open-ended answers OpenMedPrompt