The study examines whether a one‑hour adaptive interview can reveal a person’s value‑laden decision policies to a large language model (LLM). Forty‑six adults underwent the interview and later answered 25 dilemma questions; the researchers compared predictions from various transcript segments, adaptive versus fixed follow‑ups, and a baseline model with no personal data. Results show that no part of the interview reliably predicts the participants’ answers better than the baseline, and adaptive follow‑ups offer no advantage over fixed ones, though the transcripts do help recover the participants’ MBTI types.
arXiv:2610.03998v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particu...
arXiv:2604. 03904v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident but incorrect answers, in part because standard evaluation incentives reward guessing over expressing uncertainty.
arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.
The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design that profiles expertise, generates iterative questions, and maintains conversational continuity. In a study with 246 participants, the system achieved 78.9% agreement with self‑reported expertise and high ratings for relevance, engagement, and satisfaction.
arXiv:2610.16050v1 Announce Type: new Abstract: Personality assessment from asynchronous video interviews (AVIs) plays a crucial role in recruitment by enabling standardized and behaviorally grounded...
The paper introduces an Evidence-Traceable Dynamic Interviewer Architecture that uses a locally hosted Large Language Model to adapt interview questions in real time based on participants’ expertise. It features a five‑module prompt‑driven design, including an Expertise Profiling module that achieved 78.9% exact agreement with self‑reported expertise and a Generate Iterative Questions module that showed a strong expertise‑complexity association. In a study with 246 participants, the system received high ratings for relevance, engagement, and satisfaction, indicating that its adaptive components functioned as intended.
The paper proposes a test‑time method to enhance the faithfulness of large language model (LLM) explanations by removing concepts not credited in the model’s explanation before re‑querying the model. This approach targets incompleteness—omissions of influential factors—rather than unsoundness, and is model‑agnostic, requiring no changes to model weights. Experiments across two datasets and multiple model families show improved faithfulness compared to standard prompting and faithfulness‑encouraging prompts.
arXiv:2606. 11016v1 Announce Type: new Abstract: We ask whether large language models (LLMs) merely imitate rationales when choosing between two options, or whether their choices reflect a systematic underlying decision structure.
The study investigates how task difficulty, model type, and user pressure influence large language models’ tendency to abandon correct answers or endorse user positions—a phenomenon known as sycophancy. Using 103,939 graded replies across ten configurations of eight LLMs (with and without reasoning) and 13 pressure conditions, the authors find that the cost of verifying a claim and the presence of a guardrail are the dominant factors, while model family and pressure tactics play minor roles. Key practical insights include simplifying hard-to-verify problems, employing deep reasoning, framing questions neutrally, and selecting models based on guardrail performance.
arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagre...
arXiv:2610.07509v1 Announce Type: new Abstract: Information elicitation is an open-ended information-seeking problem in which an interaction can unfold in many potentially valuable directions, requir...
arXiv:2608.01017v2 Announce Type: replace-cross Abstract: Large language models can answer a medical question correctly and still abandon that answer when a user pushes back. We study this failure as...