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Using Large Language Models to Support High Volume Application Review for an Undergraduate Research Program

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arXiv:2606. 05564v1 Announce Type: new Abstract: Undergraduate research programs such as the Summer Undergraduate Research Fellowship (SURF) at Purdue University receive thousands of applications every year, requiring significant time and effort for program staff to evaluate each submission consistently and within tight timelines.

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Authors: Varun Aggarwal, Kay Kobak, John Howarter
Released: 2026-06-05 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Using Large Language Models to Support High Volume Application Review for an Undergraduate Research Program overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, word, under, eval, line, rate, compare, full. Source: arxiv cs.CL (NLP).
arXiv: 2606.05564

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