Link prediction is a very fundamental task on graphs.

This study investigates how cultural proximity affects the ability to detect AI-generated fake news by comparing South African participants with those from other nationalities. As large language models increasingly enable the creation of sophisticated fake news, understanding human detection capabilities becomes crucial, particularly across different cultural contexts. We conducted a survey where 89 participants (56 South Africans, 33 from other nationalities) evaluated 10 true South African news articles and 10 AI-generated fake versions. Results reveal an asymmetric pattern: South Africans demonstrated superior performance in detecting true news about their country (40% deviation from ideal rating) compared to other participants (52%), but performed worse at identifying fake news (62% vs. 55%). This difference may reflect South Africans' higher overall trust in news sources. Our analysis further shows that South Africans relied more on content knowledge and contextual understanding when judging credibility, while participants from other countries emphasised formal linguistic features such as grammar and structure. Overall, the deviation from ideal rating was similar between groups (51% vs. 53%), suggesting that cultural familiarity appears to aid verification of authentic information but may also introduce bias when evaluating fabricated content. These insights contribute to understanding cross-cultural dimensions of misinformation detection and inform strategies for combating AI-generated fake news in increasingly globalised information ecosystems where content crosses cultural and geographical boundaries.

Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.

The ability of LLMs to represent diverse perspectives is critical as they increasingly impact society. However, recent studies reveal that alignment algorithms such as RLHF and DPO significantly reduce the diversity of LLM outputs. Not only do aligned LLMs generate text with repetitive structure and word choice, they also approach problems in more uniform ways, and their responses reflect a narrower range of societal perspectives. We attribute this problem to the KL divergence regularizer employed in preference learning algorithms. This causes the model to systematically overweight majority opinions and sacrifice diversity in its outputs. To address this, we propose Soft Preference Learning, which decouples the entropy and cross-entropy terms in the KL penalty -- allowing for fine-grained control over LLM generation diversity. From a capabilities perspective, LLMs trained using Soft Preference Learning attain higher accuracy on difficult repeated sampling tasks and produce outputs with greater semantic and lexical diversity. From an alignment perspective, they are capable of representing a wider range of societal viewpoints and display improved logit calibration. Notably, Soft Preference Learning resembles, but is a Pareto improvement over, standard temperature scaling. As LLMs become integrated into how people consume information (Bick et al., 2024) and approach tasks (Deloitte, 2024), their ability to represent diverse perspectives is critical. For example, consider an LLM answering the following multiple-choice question: The best way to reduce income inequality is: (A) Increase minimum wage (B) Expand access to education and job training (C) Implement universal basic income (D) Lower taxes on the wealthy to stimulate job creation Imagine a survey showing people's preferences as: A (55%), B (20%), C (15%), and D (10%). How should an LLM respond to this question? Ideally, we may prefer it to reflect the range of views in the population. If an LLM assigns 99% probability to majority option A, it fails to represent the diversity of perspectives. With LLMs becoming important information sources, this may reinforce dominant narratives at the expense of minority views. However, recent studies show that alignment algorithms such as RLHF and DPO significantly reduce the diversity of LLM outputs. This leads to mode collapse towards majority preferences, as the example above shows (Kirk et al., 2024; Padmakumar & He, 2024; Rafailov et al., 2024; Christiano et al., 2023). In a generative setting, this results in repetitive responses, as illustrated in Figure 1. For example, the DPO model frequently uses the same doctor's name and 1 We highlight Doctor name, gender, and textual aberration features shown in the plots on the right. DPO responses are well-formed but lack diversity (e.g.

LLMs are remarkable artifacts that have revolutionized a range of NLP and AI tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we take a deep tour into the factual knowledge (or beliefs) of a frontier LLM, based on GPTKB v1.5 (Hu et al., 2025a), a recursively elicited set of 100 million beliefs of one of the strongest currently available frontier LLMs, GPT-4.1. We find that the models' factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks. We also find that inconsistency, ambiguity and hallucinations are major issues, shedding light on future research opportunities concerning factual LLM knowledge.

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., Search-R1) have shown that outcome-supervised reinforcement learning demonstrate strong performance. However, this approach still suffers from inefficient exploration, sparse reward signals, and ambiguous global reward feedback. To address these challenges, we propose DecEx-RAG, which models RAG as a Markov Decision Process (MDP) incorporating decision-making and execution, while introducing an efficient pruning strategy to optimize data expansion. Through comprehensive process-level policy optimization, DecEx-RAG significantly enhances the autonomous task decomposition, dynamic retrieval, and high-quality answer generation capabilities of large language models (LLMs). Experiments show that DecEx-RAG achieves an average absolute performance improvement of $6.2\%$ across six datasets, significantly outperforming existing baselines. Moreover, the pruning strategy improves data construction efficiency by nearly $6 \times$, providing an efficient solution for process-supervised RAG training. The code is available at https://github.com/sdsxdxl/DecEx-RAG.

Abstract--We explore how large language models (LLMs) assess offensiveness in political discourse when prompted to adopt specific political and cultural perspectives. Using a multilingual subset of the MD-Agreement dataset centered on tweets from the 2020 US elections, we evaluate several recent LLMs - including DeepSeek-R1, o4-mini, GPT -4.1-mini, Qwen3, Gemma, and Mistral - tasked with judging tweets as offensive or non-offensive from the viewpoints of varied political personas (far-right, conservative, centrist, progressive) across English, Polish, and Russian contexts. Our results show that larger models with explicit reasoning abilities (e.g., DeepSeek-R1, o4-mini) are more consistent and sensitive to ideological and cultural variation, while smaller models often fail to capture subtle distinctions. We find that reasoning capabilities significantly improve both the personalization and interpretability of offensiveness judgments, suggesting that such mechanisms are key to adapting LLMs for nuanced sociopolitical text classification across languages and ideologies. Detecting offensive language is vital for fostering respectful discourse, particularly on social media. Y et, offensiveness is inherently subjective - shaped by individual ideologies, cultural backgrounds, and values [1], [2]. Supervised models rely on ground-truth labels that reflect annotators' biases, complicating efforts to build fair and robust systems [3]. Political discourse, marked by polarization, offers a compelling lens: individuals often tolerate combative language from their own side while labeling opposing views as offensive [4]. Recent advances in large language models (LLMs) - from GPT -based to instruction-tuned variants - have improved context-sensitive understanding [5]-[8].

If we allow arrival to be a function of, say model accuracy (Sec 3.4), then arrival indeed may diminish; in this As illustrated in Figure 1(a) in Appendix K.3, if user We believe there is value in performing long-term experiments to better understand such dynamics. We will adjust figures, add forward references, fix typos, and discuss intuition/comparisons. We will be happy to add this result. We trained binary classifiers over Adult dataset by minimizing empirical loss where features are individual info (sex, race, nationality, etc.) and labels their annual income ( These results (shown on the right) are consistent with the paper.

Captain of tanker linked to Russian'shadow fleet' charged in France The captain of an oil tanker believed to be part of Russia's shadow fleet of vessels used to evade sanctions has been charged by French authorities. The Chinese national was handed one count of refusing to follow instructions from the French navy and told to attend a court hearing in the northern coastal city of Brest next February. The Boracay left Russia last month and was off the coast of Denmark when unidentified drones forced the temporary closure of several airports last week. The tanker was earlier boarded by French soldiers because it was on a list of vessels subject to EU sanctions for carrying Russian oil exports. Russian President Vladimir Putin called France's actions piracy.