most citedAmulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs

1 citations · 2 across the 7 of their papers we have counts for

collaborators

8 papers

cs.AI2025

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Chandler Smith, Marwa Abdulhai, Manfred Diaz +83

Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…

cs.AI2025

Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems

Mingwei Li, Xiaoyuan Zhang, Chengwei Yang +2

Model-based planning in robotic domains is challenged by the hybrid nature of physical dynamics, where continuous motion is punctuated by discrete events such as contacts and impac…

eess.AS2025

UltraVoice: Scaling Fine-Grained Style-Controlled Speech Conversations for Spoken Dialogue Models

Wenming Tu, Guanrou Yang, Ruiqi Yan +6

Spoken dialogue models currently lack the ability for fine-grained speech style control, a critical capability for human-like interaction that is often overlooked in favor of purel…

cs.CL2025

Make an Offer They Can't Refuse: Grounding Bayesian Persuasion in Real-World Dialogues without Pre-Commitment

Buwei He, Yang Liu, Zhaowei Zhang +6

Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or rely on unrealistic pre-commitmen…

cs.CY2025

PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives

Zhaowei Zhang, Xiaobo Wang, Minghua Yi +5

Achieving political consensus is crucial yet challenging for the effective functioning of social governance. However, although frontier AI systems represented by large language mod…

cs.CL2025★ 1 cited

Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs

Zhaowei Zhang, Fengshuo Bai, Qizhi Chen +5

How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, chang…

cs.AI2024

Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment

Lu Chen, Yuxuan Huang, Yixing Li +6

This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrec…

cs.CL2024★ 1 cited

MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark

Hongwei Liu, Zilong Zheng, Yuxuan Qiao +7

Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional p…