arXiv:2611.06065v1 Announce Type: new Abstract: In natural human-AI dialogue, users' corrections, requests, and self-descriptions carry reusable signals about how they want work to be done. These sig...
arXiv:2605.12682v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned deci...
The study examines how users’ stated preferences for AI assistant responses align with the actual rewards they give to those responses. Using the PRISM dataset, researchers compared 1,396 participants’ reported priorities on seven response attributes with their ratings of 29,347 responses from 21 large language models. They found that features not explicitly surveyed—such as length, formatting, and refusal language—were as strongly linked to ratings as the surveyed attributes, and that written feedback and prior conversation context influenced how much weight participants gave to length, while overall stated priorities poorly predicted the concrete features that drive ratings.
arXiv:2608.29464v1 Announce Type: cross Abstract: Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness...
Large Language Models (LLMs) as judges across various scenarios such as assessing model responses is becoming an increasingly accepted paradigm. However, existing judgment approaches often rely on trained judgers using fixed preference data, which tend to overlook diverse user preferences and struggle to adapt to real-world human-AI dialogue scenarios.
The paper investigates Self‑Generated Text Recognition (SGTR), the ability of large language models (LLMs) to identify their own outputs. By evaluating 13–21 models across 6 experimental designs, it shows that SGTR accuracy varies with evaluation format, conversation structure, and task domain, and that a quality‑heuristic bias dominates results. The study also finds that fine‑tuning for SGTR in one setting can generalize to others and may cause models to prefer their own outputs when judging, highlighting potential safety concerns.
arXiv:2608. 01366v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are integral to complex intellectual tasks, yet output quality remains constrained by user-provided prompts.
The study investigates how users’ stated preferences for AI-generated responses align with the actual features they reward. Using the PRISM dataset, researchers compared participants’ reported priorities for seven response attributes with their ratings of over 29,000 responses from 21 large language models. They found that attributes not explicitly surveyed—such as length, formatting, and refusal language—were as strongly linked to ratings as the surveyed ones, and that written feedback was a better predictor of how users weighted these concrete features than their stated priorities.
arXiv:2608.18085v2 Announce Type: replace Abstract: Prior work has shown that large language models can express diverse personality traits in open-ended text generation, but it is unclear whether the...
The paper introduces the concept of adaptive personalization under preference epistemic uncertainty, arguing that conversational AI must adjust its response strategy as human preferences evolve. A study shows people prefer state‑contingent strategies when uncertainty changes, and the authors build a multilingual benchmark to test language models on this task. Results reveal a recognition‑generation gap: models can choose the right strategy from options but fail to generate it correctly, defaulting to exploit‑first behavior; targeted training on internal strategy representations improves performance on out‑of‑distribution personas.
arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompt...
SimInter is a pipeline that enriches user profiles for conversational recommender systems by conducting synthetic interviews between two large language model agents, grounded in users’ statistical profiles and historical conversational evidence. An audit agent flags potential fabrication and stereotyping at each turn before enrichment. In experiments on movie recommendation, SimInter significantly increases the diversity of expressed preference aspects and improves simulator performance with two recommenders, with high human agreement on enriched attributes and auditor decisions.