The paper investigates how much of an AI model’s expressed preferences are due to the model itself versus the instrument (prompt) used to elicit those preferences. By fixing the set of outcomes and models while varying five different prompting instruments across 15 welfare-related outcomes, the study finds that the ranking of outcomes is only moderately generalizable (coefficient 0.348) and that a single instrument’s preference provides little insight into another instrument’s results. The analysis shows that even after removing any single instrument, model, or a subset of outcomes, the overall preference estimate remains robust, yet the variability across instruments remains significant.
By Jason Hung
The paper introduces a pluralistic agreement index, Gamma, to quantify how often wrong runs of large language models (LLMs) agree with the majority consensus. By decomposing Gamma into a mechanical component and a preference‑unexplained residual, the authors show that on GPT‑4.1 the mechanical part explains most of the agreement on multiple‑choice benchmarks but only about half on open‑domain tasks, revealing a residual bias that can cause self‑consistency to backfire on hard questions. The study provides a quantitative framework for understanding when majority voting over LLM samples improves or harms accuracy, without proposing new voting methods.
By Lizhuo Zhang, Mengmeng Tang, Chenfeng Long, Xiaoyong Tang, Xiang Luo
The paper demonstrates that a preference‑optimization objective can learn to distinguish reliable from unreliable sources by installing a prior‑dependent reliability switch. By training on data where a source’s stated reliability is paired with its answer, the model learns to flip its response only when the stated reliability exceeds a threshold that grows with the model’s prior. Experiments on Qwen2.5‑7B‑Instruct and Llama‑3.1‑8B show that this switch generalizes to unseen reliability values and follows stated reliability over role prestige, whereas supervised imitation fails to learn it.
By Sen Yang, Yuen-Hei Yeung
arXiv:2607. 02104v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise.
By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao
The study analyzes 373,019 judgments from LLM‑scored benchmarks, decomposing variance into system, item, judge, and interaction components via generalizability theory. It finds that with a single judge, generalizability converges to a ceiling determined by the system‑by‑judge variance, which is substantially lower in pairwise preference settings, allowing one judge to suffice. The research also reveals significant biases in presentation order and highlights that many published win‑rate claims fall below the measured floor of the benchmarks.
By Atul Anand
arXiv:2609.37472v1 Announce Type: cross
Abstract: Behavioral tests measure how a language model reads evidence. We ask whether those measurements help choose a recommendation interface. We evaluate s...
By Han Chen, Yingrui Li
arXiv:2605. 03534v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer.
By Jingxi Qiu, Zeyu Han, Cheng Huang
arXiv:2605. 27914v2 Announce Type: replace-cross Abstract: Benchmarking is mature where answers are verifiable -- math, code, reasoning -- but the fastest-growing uses of LLMs are subjective and human-facing: companionship, emotional support, counseling.
By Yuming (Rapheal), Huang, Yao Liu, Pengjie Ding, Lei Wang, Junchen Wan
The article examines how a difference‑in‑differences (DiD) analysis on a censored rating scale can produce misleading effects. It demonstrates that each DiD component is censored by its own share, causing differential attenuation that can fabricate an interaction effect when the two responses are unequally censored. Using a pre‑registered audit of an LLM judge, the authors show that the reported significant interaction is largely an artifact of this censoring mechanism, with the true preference effect being null.
By Shuyi Fan, Boyuan Deng, Mengyu Xu, Xinhong Xie, Chenyang Li, Hongyang Zhang
arXiv:2607. 28685v1 Announce Type: new Abstract: Agent-safety benchmarks measure different behaviors, and their scores get quoted interchangeably as an agent's safety.
By Youting Wang, Xiao Han, Dingyan Shang, Yuan Tang, Bowen Liu
arXiv:2607. 11920v1 Announce Type: cross Abstract: Evaluating decisions made under uncertainty is hard when labeled outcomes are scarce, costly, or confounded with luck.
By Jeff Helzner
arXiv:2607. 10202v1 Announce Type: new Abstract: Cross-model comparisons read divergence in value dispositions as evidence that language models hold individuated values.
By Hong-In Won, Jinseok Jang, Hyoseop Kim