Evaluating LLM-Based Grant Proposal Review via Structured Perturbations
As AI-assisted grant proposals outpace manual review capacity in a kind of “Malthusian trap” for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation.- doi
- 10.1145/3800935.3830838
- isbn
- 979-8-4007-2564-7
- name
- Evaluating LLM-Based Grant Proposal Review via Structured Perturbations
- source
- pdf_layout
- acm_url
- https://dl.acm.org/doi/10.1145/3800935.3830838
- authors
- William Thorne, Joseph James, Yang Wang, Chenghua Lin, Diana Maynard
- doi_url
- https://doi.org/10.1145/3800935.3830838
- license
- CC BY 4.0
- summary
- As AI-assisted grant proposals outpace manual review capacity in a kind of “Malthusian trap” for the research ecosystem, this paper investigates the capabilities and limitations of LLM-based grant reviewing for high-stakes evaluation.
- published
- 2026-09-14
- conference
- HT '26: 37th ACM Conference on Hypertext, London, United Kingdom, September 14–18, 2026
- open_access
- true
- acm_html_url
- https://dl.acm.org/doi/fullHtml/10.1145/3800935.3830838
- displayAuthor
- William Thorne, Joseph James, Yang Wang, Chenghua Lin, Diana Maynard
- proceedings_url
- https://dl.acm.org/doi/proceedings/10.1145/3800935
- displayPublishTime
- 2026-09-14
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