arXiv:2509. 22888v2 Announce Type: replace Abstract: Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature.
By Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan, Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang
The paper proposes a response‑free method for estimating difficulty of reading‑comprehension multiple‑choice items by fine‑tuning a transformer on item wording. It introduces two extensions to a baseline joint‑encoding model: a component‑wise variant that encodes passage, question, and options separately, and a multi‑task variant that adds a question‑answering auxiliary task. Experiments on a corpus of nearly 30,000 items show that both extensions outperform the baseline, especially the multi‑task variant across all metrics and the component‑wise variant in rank ordering, even with limited training data.
By Jan Net\'ik, Patr\'icia Martinkov\'a
The paper introduces the concept of LLM‑specific utility, defining it as the performance gain a target large language model (LLM) achieves when provided with a passage compared to answering without evidence. A benchmark of utilitarian passages is built for four LLMs (Qwen3‑8B/14B/32B and Llama 3.1‑8B) across three QA datasets, revealing that each model benefits most from its own tailored evidence and that evidence optimized for other models is consistently suboptimal. The authors also create SpecUBench, a benchmark for LLM‑specific utility judgment, and show that current utility‑aware retrieval methods largely capture model‑agnostic usefulness, struggling to estimate LLM‑specific utility.
"whyItMatters":"The study demonstrates that retrieval‑augmented generation must consider model‑specific evidence selection to truly improve LLM performance, highlighting a gap in existing utility‑aware methods."
By Hengran Zhang, Keping Bi, Jiafeng Guo, Jiaming Zhang, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng
arXiv:2512. 07019v3 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications and actionable future improvements.
By Zhiyu Xu, Jia Liu, Yixin Wang, Yuqi Gu
The paper introduces a dual‑dimensional framework called Automated Item Similarity Analysis (AISA) that uses Large Language Models to assess incidental content similarity in large‑scale assessments. It combines Structured Decomposition and Semantic Relatedness to capture both structural and semantic nuances that traditional metrics miss. Psychometric validation shows that LLM‑derived metrics better align with construct‑irrelevant local dependence and produce more coherent item groupings, and simulations in Computerized Adaptive Testing demonstrate improved estimation stability and reduced bias with minimal efficiency loss.
By Jing Huang, Jihong Zhang, Hua-Hua Chang
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.
By Christopher Ormerod