arXiv Computation and Language
Oct 12

Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection

The paper investigates whether language models can learn to abstain from answering when uncertain, focusing on Qwen3-14B for factual questions. Two approaches are examined: training the model itself and training a separate answer selector. The learned selector improves utility over the model’s calibrated confidence filter on TriviaQA, raising coverage from 20.2% to 30.9% and reducing error rate from 23.0% to 14.5%, though gains are inconsistent across datasets and reinforcement‑learning changes do not consistently help.

By Juan Terven, Diana-Margarita C\'ordova-Esparza, Ruben Gomez, Carlos Miranda Rocha, Julio-Alejandro Romero-Gonz\'alez, Edgar Chavez-Urbiola
arXiv AI
Sep 25

PROOF: Profiling Reliability of Object-Level Facts in Large Language Models

PROOF is a benchmark that profiles the reliability of object-level facts in instruction-tuned language models by converting a frozen Wikidata snapshot into 18,486 English multiple-choice questions grounded in 11,779 semantic facts across 101 classes, 392 properties, and 14 domains. Each question includes an explicit "I don't know" option, a "No correct option" control, and nine controlled formulations, with 1,849 questions designed as no-correct-option traps. The study evaluates 18 open-weight model deployments on 166,374 prompts, revealing wide variability in factual accuracy, sensitivity to wording changes, and the impact of decoder perturbations.

By Andrei Chetvergov, Mikhail Solovev, Timofei Sivoraksha, Stepan Ukolov, Valeriia Kuschenko, Alexander Evseev, Sergey Bolovtsov
arXiv AI
Jun 10

TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning

arXiv:2509. 25760v2 Announce Type: replace-cross Abstract: While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric knowledge.

By Zhepei Wei, Xiao Yang, Kai Sun, Jiaqi Wang, Rulin Shao, Jingxiang Chen, Mohammad Kachuee, Teja Gollapudi, Yiwei Liao, Nicolas Scheffer, Rakesh Wanga, Anuj Kumar, Yu Meng, Wen-tau Yih, Xin Luna Dong