The study evaluates how large language models handle confident fabrication versus abstention when predicting pain from speech transcripts that contain no lexical pain cues. Using the TAME Pain speech corpus, researchers found that while acoustic features could predict pain, transcript‑only predictions were near chance, indicating unsupported claims. Models prompted cooperatively largely abstained on non‑pain transcripts and accurately extracted pain ratings when present, whereas authority‑framed prompts led to variable abstention and some models (Gemini 2.5 Flash, Llama 3.1 8B) produced confident yet fabricated pain scores.
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.
arXiv:2606. 29490v1 Announce Type: cross Abstract: Confidence is an estimate of the probability that a chosen answer is correct.
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.
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.
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.
arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
arXiv:2605. 00865v2 Announce Type: replace-cross Abstract: We tested whether auditory-evoked EEG supports subject-independent five-vowel perception decoding when trial identity, model identity, prediction provenance, and participant-level inference are controlled within a single benchmark.
arXiv:2606. 05970v1 Announce Type: cross Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks.
arXiv:2608.03854v4 Announce Type: replace Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...
The paper investigates whether language‑grounded explanations improve trust in automated sheep pain detection from facial expressions. It finds that attention‑based explanations tied to the Sheep Pain Facial Expression Scale (SPFES) are largely ineffective, prompting the authors to replace the appearance bypass with a concept bottleneck that uses SPFES concept scores supervised by per‑region annotations. This new architecture slightly reduces performance but yields more semantically meaningful concepts, recovering minority pain states and revealing ear‑and‑eye severity orderings without explicit severity supervision, and highlights the pitfalls of pooled concept accuracy under clinical imbalance.
The study examined whether providing full prompt-level context to a large multimodal model would improve speech transcription accuracy on a production oral‑history corpus. Using a preregistered within‑item paired ablation, the authors found that adding context did not produce a detectable change in side‑level word error rate (WER) for either gpt‑4o‑transcribe or gemini‑2.5‑flash. The results suggest that context alone may not be sufficient to enhance aggregate transcription accuracy, and that finer‑grained, sequence‑aligned metrics are needed to evaluate such mechanisms.
arXiv:2608. 14552v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty.