The paper proposes using large language models (LLMs) to identify disagreements among models as a way to focus expert effort on revising codebooks for large‑scale text annotation. Three expert feedback methods are evaluated: editing LLM‑generated revisions (Codebook Verifying), answering questions about disagreements (Question Answering), and labeling disagreement cases with rationales (Rationale Labeling). Experiments on tutoring‑session transcripts show that Rationale Labeling achieves the highest LLM‑labeling accuracy (64.9%) compared to the expert‑revised codebook (57.8%), with Question Answering also outperforming the baseline (60.5%).
By Zeyu He, Zhuqian Zhou, Kirk Vanacore, Rene F. Kizilcec, Ting-Hao 'Kenneth' Huang
Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon we attribute to fact perturbations in the input....
The paper introduces a fixed‑budget revision protocol that uses deterministic verifiers to expose all remaining violations across exact‑length, lexical, and compositional constraints, thereby isolating model‑side revision behavior. Experiments on 19 open‑ and closed‑source LLMs show wide variability in controller‑level success, with some models achieving up to 99.8% success while others remain below 20%. Controlled studies reveal that post‑training and scale affect model responses to exact feedback, but do not consistently improve exact correction, and that recurrence of earlier outputs is linked to lower recoverability.
By Haitong Jiang, Chunlin Liu, Yile Wang, Yuhong Feng
CriticGen introduces a generation‑aware evaluation framework that generates sample‑specific evaluation dimensions and scoring criteria across categories such as subjective, objective, and self‑derived constraints. These dynamic rubrics produce a score, reason, executable refinement suggestion, and a refined answer, enabling models to diagnose and target flaws in their responses. Experiments show significant gains in rubric quality, score correlation, and actionable feedback, with 73.17% of answers improved and a 93.28% non‑degradation rate.
By Huifang Du, Zecheng Zuo, Sen Wang, Chenghao Fan, Haofen Wang, Yehui Yang
The paper introduces a fine-grained method called interactions to analyze prompt sensitivity in large language models (LLMs). By decomposing output scores into nonlinear interactions, the authors show that subtle prompt changes can destabilize these interactions even when overall outputs stay unchanged. They propose an Interaction-based Prompt Sensitivity (IPS) metric and use it to evaluate 50 open-source LLMs, finding that supervised fine‑tuning, larger model scales, dense architectures, and few‑shot learning all reduce prompt sensitivity, primarily by stabilizing low‑order interactions.
By Ruiyang Qin, Qingzhuo Wang, Tian Wang, Zhihua Wei, Wen Shen
The paper introduces DEDUCE, a three‑stage framework that turns large language models into proactive error correctors by detecting input fact errors, devising correction strategies, and delivering reliable answers. It also presents MisFactQA, a dataset of factual errors, and new metrics for robustness evaluation. Experiments on TruthfulQA, FalseQA, and MisFactQA show significant gains in accuracy and error correction across Qwen, LLaMA, and Gemma models.
By Ping Wang, Xiangguo Sun, Bingbing Xu, Guocong Li, Xiaofeng Meng
arXiv:2610.01428v1 Announce Type: cross
Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...
By Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein, Rom Himelstein, Avi Mendelson, Amit LeVi
iFlip is an iterative refinement method for generating counterfactual examples using large language models. It incorporates three feedback types—model confidence, feature attribution, and natural language—to guide successive edits. Experiments show iFlip outperforms five state‑of‑the‑art baselines, achieving a 57.8% higher validity rate and improving model performance through counterfactual data augmentation.
By Yilong Wang, Qianli Wang, Nils Feldhus
arXiv:2606. 32029v1 Announce Type: cross Abstract: While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.
By Yuqing Yang, Qi Zhu, Zhen Han, Boran Han, Zhengyuan Shen, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
arXiv:2510. 12857v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide.
By Robin Staab, Jasper Dekoninck, Maximilian Baader, Martin Vechev
arXiv:2606.12234v2 Announce Type: replace
Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the invol...
By Iuri Macocco, Pau Rodr\'iguez, Arno Blaas, Luca Zappella, Marco Baroni, Xavier Suau
arXiv:2609.24516v1 Announce Type: new
Abstract: In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these s...
By Khaoula Chehbouni, Melina Medjdoub, Florian Carichon, Golnoosh Farnadi, Jackie Chi Kit Cheung