most citedAn Evaluation of Cultural Value Alignment in LLM

2 citations · 3 across the 5 of their papers we have counts for

collaborators

6 papers

cs.AI2025

Rationality Check! Benchmarking the Rationality of Large Language Models

Zhilun Zhou, Jing Yi Wang, Nicholas Sukiennik +4

Large language models (LLMs), a recent advance in deep learning and machine intelligence, have manifested astonishing capacities, now considered among the most promising for artifi…

cs.AI2025

A Survey of Large Language Model-Powered Spatial Intelligence Across Scales: Advances in Embodied Agents, Smart Cities, and Earth Science

Jie Feng, Jinwei Zeng, Qingyue Long +15

Over the past year, the development of large language models (LLMs) has brought spatial intelligence into focus, with much attention on vision-based embodied intelligence. However,…

cs.CY20252 cited

An Evaluation of Cultural Value Alignment in LLM

Nicholas Sukiennik, Chen Gao, Fengli Xu +1

LLMs as intelligent agents are being increasingly applied in scenarios where human interactions are involved, leading to a critical concern about whether LLMs are faithful to the v…

cs.IR20251 cited

Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents

Nicholas Sukiennik, Haoyu Wang, Zailin Zeng +2

An increasing reliance on recommender systems has led to concerns about the creation of filter bubbles on social media, especially on short video platforms like TikTok. However, th…

cs.AI2024

SmartAgent: Chain-of-User-Thought for Embodied Personalized Agent in Cyber World

Jiaqi Zhang, Chen Gao, Liyuan Zhang +2

Recent advances in embodied agents with multimodal perception and reasoning capabilities based on large vision-language models (LVLMs), excel in autonomously interacting either rea…

cs.IR2024

LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation

Shutong Qiao, Chen Gao, Wei Yuan +2

Sequential recommendation (SR) leverages users' dynamic preferences, with recent advances incorporating multi-interest learning to model diverse user interests. However, most multi…