  Many large language models (LLMs) for medicine have largely been evaluated on
short texts, and their ability to handle longer sequences such as a complete
electronic health record (EHR) has not been systematically explored. Assessing
these models on long sequences is crucial since prior work in the general
domain has demonstrated performance degradation of LLMs on longer texts.
Motivated by this, we introduce LongBoX, a collection of seven medical datasets
in text-to-text format, designed to investigate model performance on long
sequences. Preliminary experiments reveal that both medical LLMs (e.g., BioGPT)
and strong general domain LLMs (e.g., FLAN-T5) struggle on this benchmark. We
further evaluate two techniques designed for long-sequence handling: (i)
local-global attention, and (ii) Fusion-in-Decoder (FiD). Our results
demonstrate mixed results with long-sequence handling - while scores on some
datasets increase, there is substantial room for improvement. We hope that
LongBoX facilitates the development of more effective long-sequence techniques
for the medical domain. Data and source code are available at
https://github.com/Mihir3009/LongBoX.
