The paper evaluates the robustness of automated fact‑checking systems by cross‑benchmarking nine models—including random baselines, fine‑tuned transformers, zero‑shot LLMs, and top AVeriTeC 2025 systems—across four datasets from scientific, open‑web, and climate domains. It finds that fine‑tuned models outperform zero‑shot LLMs on ClimateCheck, that system rankings vary strongly with domain and metric, and that replacing retrieved evidence with gold annotations boosts veracity accuracy by 14–22 points, underscoring retrieval as the main bottleneck. The authors provide code, pre‑processed datasets, and results to enable reproducible research.
By Aida Usmanova, Zangir Iklassov, Markus Leippold, Ricardo Usbeck
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
By Selen Erkan, Bastian Boll, Kristian Kersting, Bj\"orn Deiseroth, Letitia Parcalabescu
arXiv:2608.22483v1 Announce Type: new
Abstract: Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is mis...
By Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang, Li Chen
arXiv:2410. 13341v4 Announce Type: replace Abstract: High quality annotations are increasingly a bottleneck in the explosively growing machine learning ecosystem.
By Florian E. Dorner, Vivian Y. Nastl, Moritz Hardt
Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a single benchmark and cannot be trusted to general...
arXiv:2608. 08700v1 Announce Type: new Abstract: Reliable evaluation of tool routing is critical as Large Language Models increasingly operate as autonomous agents.
By Dongjie Xu, Julius, Hanchi Dong, Minghua Tang, Yuxuan Sun, Ziwei Nie, Zicheng Liu, Dujun Qing, Jiajie Xu
arXiv:2610.16152v1 Announce Type: cross
Abstract: Although post-training significantly enhances model performance, the use of massive undisclosed data raises concerns about benchmark contamination an...
By Hengxiang Zhang, Junxian Liu, Hongxin Wei
arXiv:2610.27566v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts. For respon...
By Nima Seifi, Martin Ester
arXiv:2606. 09856v1 Announce Type: cross Abstract: Post-training Large Language Models (LLMs) for reasoning typically focuses on deductive tasks such as mathematics and coding where correctness is verifiable.
By Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake, Thomas L. Griffiths
S-EDL is a new evidential method that calibrates large language models for open‑ended generation by using the model’s own sequence likelihoods to build a differentiable Dirichlet prior over prompt‑specific semantic classes. This approach optimizes an evidential loss that provides a calibration‑aware training signal without relying on fixed multiple‑choice options. Experiments on four open‑ended benchmarks and three models show that S-EDL reduces semantic calibration error and improves accuracy, offering a framework for trustworthy LLM deployment in complex applications.
By Yawei Li, Jiazheng Li, David R\"ugamer, Bernd Bischl, Yulan He, Mina Rezaei
arXiv:2601. 07965v2 Announce Type: replace Abstract: When a model knows when it does not know, many possibilities emerge.
By Chenjie Hao, Weyl Lu, Yuko Ishiwaka, Zengyi Li, Weier Wan, Yubei Chen
MarkTune is a model‑embedded watermarking framework that improves the trade‑off between generation quality and detectability. By regularizing toward a high‑quality reference distribution, it decouples quality preservation from proximity to the base model and uses the GaussMark detection statistic as a reward during on‑policy fine‑tuning. Empirical results show that MarkTune enhances the quality‑detectability frontier, maintains downstream task performance, and remains robust against text‑editing and open‑weight attacks across diverse datasets.
By Yizhou Zhao, Zhiwei Steven Wu, Adam Block