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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.