AI’s affect on communities, students, staff - USI | student newspaper
Hey there, little explorer! 🚀
Imagine we have a super-duper smart robot friend named AI! 🤖
This newspaper story is like a chat about how AI is visiting our schools and towns. It's asking: "What happens when AI comes to play with the kids at school, the teachers, and everyone in our neighborhood?"
Maybe AI can help with homework like a super-fast helper, or maybe it helps teachers find cool new games. But we also need to make sure AI plays fair and is kind to everyone.
It's like when a new friend comes to your playground – everyone wants to know how they'll play together! 🥳
AI’s affect on communities, students, staff USI | student newspaper
Could not retrieve the full article text.
Read on Google News: Generative AI →Google News: Generative AI
https://news.google.com/rss/articles/CBMihAFBVV95cUxQdkxDODdyVF9sN3pkeEpsbjFQdmh0RV9URmhMbXFrNzludzBPdk1kaWMyekdqaU1GaFgxSEFkUGh2QVU2TXRDTGd4elJsNHBOcUFvaTYwOV9fNzF6OEFGT3hxZ1hBVXdCM1Q3Sy1hMlloSjVlWi15c1RpR1F3U3poeTNLRGw?oc=5Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
paper
SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models
As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for responsible AI deployment. Although bias assessment frameworks have proliferated for attributes such as race and gender, socioeconomic status bias remains significantly underexplored despite its widespread implications in the real world. We introduce SocioEval, a template-based framework for systematically evaluating socioeconomic bias in foundation models through decision-making tasks. Our hierarchical framework encompasses 8 theme — Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents' sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer's tendency toward sycophancy. These rankings are based on scores calculated using various static ( — Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah
Revealing the Learning Dynamics of Long-Context Continual Pre-training
Existing studies on Long-Context Continual Pre-training (LCCP) mainly focus on small-scale models and limited data regimes (tens of billions of tokens). We argue that directly migrating these small-scale settings to industrial-grade models risks insufficient adaptation and premature training termination. Furthermore, current evaluation methods rely heavily on downstream benchmarks (e.g., Needle-in-a-Haystack), which often fail to reflect the intrinsic convergence state and can lead to "deceptive saturation". In this paper, we present the first systematic investigation of LCCP learning dynamics — Yupu Liang, Shuang Chen, Guanwei Zhang
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Research Papers
Revealing the Learning Dynamics of Long-Context Continual Pre-training
Existing studies on Long-Context Continual Pre-training (LCCP) mainly focus on small-scale models and limited data regimes (tens of billions of tokens). We argue that directly migrating these small-scale settings to industrial-grade models risks insufficient adaptation and premature training termination. Furthermore, current evaluation methods rely heavily on downstream benchmarks (e.g., Needle-in-a-Haystack), which often fail to reflect the intrinsic convergence state and can lead to "deceptive saturation". In this paper, we present the first systematic investigation of LCCP learning dynamics — Yupu Liang, Shuang Chen, Guanwei Zhang
Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
Large language models (LLMs) often exhibit sycophancy: agreement with user stance even when it conflicts with the model's opinion. While prior work has mostly studied this in single-agent settings, it remains underexplored in collaborative multi-agent systems. We ask whether awareness of other agents' sycophancy levels influences discussion outcomes. To investigate this, we run controlled experiments with six open-source LLMs, providing agents with peer sycophancy rankings that estimate each peer's tendency toward sycophancy. These rankings are based on scores calculated using various static ( — Vira Kasprova, Amruta Parulekar, Abdulrahman AlRabah
SocioEval: A Template-Based Framework for Evaluating Socioeconomic Status Bias in Foundation Models
As Large Language Models (LLMs) increasingly power decision-making systems across critical domains, understanding and mitigating their biases becomes essential for responsible AI deployment. Although bias assessment frameworks have proliferated for attributes such as race and gender, socioeconomic status bias remains significantly underexplored despite its widespread implications in the real world. We introduce SocioEval, a template-based framework for systematically evaluating socioeconomic bias in foundation models through decision-making tasks. Our hierarchical framework encompasses 8 theme — Divyanshu Kumar, Ishita Gupta, Nitin Aravind Birur
Student-in-the-Loop Chain-of-Thought Distillation via Generation-Time Selection
Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes to smaller models remains challenging. A key difficulty is that not all teacher-generated reasoning trajectories are suitable for student learning. Existing approaches typically rely on post-hoc filtering, selecting trajectories after full generation based on heuristic criteria. However, such methods cannot control the generation process itself and may still produce reasoning paths that lie outside the student's learning capacit — Chaoqun He, Yingfa Chen, Chaojun Xiao
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!