The Verbose Context Problem in Medical Records
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Computer Science > Computation and Language
Title:The Verbose Context Problem in Medical Records
Abstract:The verbose context problem occurs when structured concepts have token-inefficient textual representations. This bottleneck is acute in population health: cohort-level analysis of longitudinal patient records requires reasoning over thousands of medically-coded events, often exceeding 400K tokens in total. We present PopMedQA, a benchmark isolating this problem through computational tasks on groups of longitudinal patient records. We construct the benchmark using neopatient, a new library for language-controlled generation of artificial patient records. Through extensive ablations -- including prompting strategies, prompt compression, and agentic decomposition -- we find that domain-independent methods fail to alleviate the verbose context problem. There remains significant opportunity to exploit domain-specific structure in language model inputs for population-scale reasoning.
| Comments: | SD4H ICML 2026 Spotlight |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.29503 [cs.CL] |
| (or arXiv:2606.29503v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29503
arXiv-issued DOI via DataCite (pending registration)
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