Paper

Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability

Unreadunread

AI summary

arXiv:2606. 15029v1 Announce Type: new Abstract: LLM judges are used to reduce the need for costly human labor in evaluating open-ended text generation.

Reading status

Priority

About this paper

Authors: Alyssa Unell, Natalie Dullerud, Naomi Boneh, Meena Jagadeesan, Tatsu Hashimoto, Nigam Shah, Sanmi Koyejo
Released: 2026-06-16 (23d ago)
Topic: Related to current project
Relation to my work: Read next because Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability 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: code, text, class, alignment, eval, rate, compare, project. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.15029

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Metric Match: A Subset Selection Approach to Evaluating LLM Judge Reliability. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.