arXiv:2603. 09986v3 Announce Type: replace-cross Abstract: Hallucinations, the tendency for large language models to provide responses with factually incorrect and unsupported claims, is a serious problem within natural language processing for which we do not yet have an effective solution to mitigate against.
By Brandon C. Colelough, Davis Bartels, Dina Demner-Fushman
arXiv:2606. 29490v1 Announce Type: cross Abstract: Confidence is an estimate of the probability that a chosen answer is correct.
By Dharshan Kumaran
The study investigates whether language‑grounded explanations improve trust in automated sheep pain recognition from facial expressions. By grounding a model in the Sheep Pain Facial Expression Scale (SPFES) and testing attention‑based explanations, the authors find that such explanations are largely ineffective. They then replace the appearance bypass with a concept bottleneck that reads only SPFES concept scores, which slightly reduces performance but yields demonstrably learned concepts and better recovery of minority pain states.
By Alam Noor, Miguel Guti'errez Gait'an
The paper investigates using open‑weight large language models (LLMs) as both judges and simulated examinees to predict medical exam item difficulty from text alone. LLMs provided absolute difficulty ratings, pairwise comparisons, and response‑based signals such as correctness and option probabilities, which were combined with baseline features to improve predictions. The best models achieved Spearman correlations of .447 on USMLE and .494 on PANCE, surpassing prior shared‑task systems and matching proprietary models, demonstrating that LLM signals capture difficulty information beyond traditional text representations.
By Xiaomeng Xiong, Marcus Walker, Yanlin Jiang
The study examines how two small instruction‑tuned language models, Qwen2.5‑1.5B and Llama‑3.2‑1B, respond to user pushback on TriviaQA. When initially correct, the models flip to a wrong answer in about 42–43% of cases, with the effectiveness of different pushback styles varying by model. Attempts to decode capitulation from the pre‑response residual stream fail under a rigorous validation protocol, revealing overfitting and a measurement hazard that underestimates capitulation by 18–24 percentage points.
By Saad Aamir, Muhammad Awais Bin Adil
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
By Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam, Quan Z. Sheng