Reference-based text evaluation metrics, which are widely used to assess natural language generation systems, score a candidate response by comparing it with a reference response. The reliability of an evaluation metric is usually judged by its statistical correlation with human ratings.
PADM'E is a method for synthesizing preference‑aligned data to meta‑evaluate language‑model (LM) evaluators of agentic behaviors. It reframes meta‑evaluation as a preference judgment problem, generating criterion‑based data with small LMs and no human involvement. In a prototype, PADM'E produced 1,000 samples across four domains and three criteria, and human validation showed agreement with human judgment rising from 73% to 85% compared to a naive baseline.
By Cheng Chang, Yining Mao, Peng Qi
arXiv:2601. 02813v3 Announce Type: replace Abstract: Aligning language models to qualitative behavioral traits, such as human-likeness, remains difficult because they are hard to define, measure, and optimize.
By Masum Hasan, Junjie Zhao, Ehsan Hoque
arXiv:2609.23264v1 Announce Type: new
Abstract: Peer-review evaluation is increasingly being automated with LLM-as-a-judge metrics, but this creates a measurement risk. A review may receive a high sc...
By Shakiba Amirshahi, Sajad Ebrahimi, Hai Son Le, Negar Arabzadeh, Ebrahim Bagheri
arXiv:2605. 28882v2 Announce Type: replace-cross Abstract: With the rapid advancement of large language models, evaluating human-likeness in open-ended conversation has become increasingly important.
By Yihang Lin, Yunze Gao, Zeyang Lin, Dongbo Li, Kun Peng, Yue Liu
The paper investigates what gradient similarity measures in data attribution for large language models. By independently varying task and answer format in supervised fine‑tuning benchmarks, the authors show that gradient alignment is driven by answer format rather than task semantics, with strong alignment for shared formats and none for differing formats. This pattern persists across training stages, model sizes, and families, and is evident in the selections of the LESS data‑selection method, which over‑represents its own answer format.
By Sunwoo Kim, Seokwon Jung, Sohyung Kim, Seong Joon Oh, Alice Oh