What is the inference efficiency tradeoff between SMoES and hard-routing MoE approaches when evaluated on lang
Fuente:
Zenodo
Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866902317740064768 |
|---|---|
| author | SOVEREIGN Research Kernel |
| author_facet | SOVEREIGN Research Kernel |
| contents | <p>Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including</p><p><strong>Research goal:</strong> What is the inference efficiency tradeoff between SMoES and hard-routing MoE approaches when evaluated on language model reasoning tasks across varying input modalities?</p><p><em>Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.</em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20433629 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | What is the inference efficiency tradeoff between SMoES and hard-routing MoE approaches when evaluated on lang SOVEREIGN Research Kernel inference efficiency tradeoff SMoES hard-routing MoE approaches evaluated <p>Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including</p><p><strong>Research goal:</strong> What is the inference efficiency tradeoff between SMoES and hard-routing MoE approaches when evaluated on language model reasoning tasks across varying input modalities?</p><p><em>Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.</em></p> |
| title | What is the inference efficiency tradeoff between SMoES and hard-routing MoE approaches when evaluated on lang |
| topic | inference efficiency tradeoff SMoES hard-routing MoE approaches evaluated |
| url | https://doi.org/10.5281/zenodo.20433629 |