- Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou · Apr 8, 2026 · Citations: 0
Human EvalAutomatic Metrics General
Pluralistic alignment has emerged as a critical frontier in the development of Large Language Models (LLMs), with reward models (RMs) serving as a central mechanism for capturing diverse human values.
- MAM-AI: An On-Device Medical Retrieval-Augmented Generation System for Nurses and Midwives in Zanzibar
Yi Ren · Jun 28, 2026 · Citations: 0
Automatic Metrics Medicine
We evaluate the exact deployed configuration with a layered methodology -- retriever, generator under oracle context, end-to-end, and latency -- scored by LLM judges validated against physician rubrics.
- PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training
Yuhan Cheng, Hancheng Ye, Hai Helen Li, Jingwei Sun, Yiran Chen · Feb 14, 2026 · Citations: 0
Automatic Metrics Coding
We propose PrivAct, a contextual privacy-aware multi-agent learning framework that internalizes contextual privacy preservation directly into models' generation behavior for privacy-compliant agentic actions.
- A Simple and Efficient Jailbreak Method Exploiting LLMs' Helpfulness
Xuan Luo, Yue Wang, Zefeng He, Geng Tu, Jing Li · Sep 17, 2025 · Citations: 0
Automatic Metrics Law
This study reveals a critical safety blind spot in modern LLMs: learning-style queries, which closely resemble ordinary educational questions, can reliably elicit harmful responses.
- TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
Truong Nguyen, Tien-Phat Nguyen, Linh Ngo Van, Duy Minh Ho Nguyen, Khoa D. Doan · May 12, 2026 · Citations: 0
Automatic Metrics General
Direct Preference Optimization (DPO) is a widely used RL-free method for aligning language models from pairwise preferences, but it models preferences over full sequences even though generation is driven by per-token decisions.
- Preference learning in shades of gray: Interpretable and bias-aware reward modeling for human preferences
Simona-Vasilica Oprea, Adela Bâra · Apr 1, 2026 · Citations: 0
Automatic Metrics General
Using the Anthropic HHRLHF dataset, we evaluate ten diverse large language models LLMs under a standard pairwise preference setting, where baseline performance remains below 0.74 ROC AUC, highlighting the difficulty of the task.
- The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
Shree Harsha Bokkahalli Satish, Christoph Minixhofer, Maria Teleki, James Caverlee, Ondřej Klejch · Mar 15, 2026 · Citations: 0
Automatic Metrics General
We present a large-scale intersectional evaluation of accent and gender bias in three SpeechLLMs using 2,880 controlled interactions across six English accents and two gender presentations, keeping linguistic content constant through voice…
- How Value Induction Reshapes LLM Behaviour
Arnav Arora, Natalie Schluter, Katherine Metcalf, Maartje ter Hoeve · May 8, 2026 · Citations: 0
Automatic Metrics General
This is done to increase utility, ensure safety, and improve the experience of the people interacting with the model.
- SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs
Sihang, Zhao, Kangrui Yu, Youliang Yuan, Pinjia He · Apr 24, 2026 · Citations: 0
Automatic Metrics Coding
To enable systematic study, we unify and formalize safe, helpful, and pedagogical behaviors with a knowledge-mastery graph and introduce SHAPE, a benchmark of 9,087 student-question pairs for evaluating tutoring behavior under adversarial…
- IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs
Chuan Guo, Juan Felipe Ceron Uribe, Sicheng Zhu, Christopher A. Choquette-Choo, Steph Lin · Mar 11, 2026 · Citations: 0
Automatic Metrics General
IH is key to defending against jailbreaks, system prompt extractions, and agentic prompt injections.
- Can Safety Emerge from Weak Supervision? A Systematic Analysis of Small Language Models
Punyajoy Saha, Sudipta Halder, Debjyoti Mondal, Subhadarshi Panda · Mar 7, 2026 · Citations: 0
Automatic Metrics General
Safety alignment is critical for deploying large language models (LLMs) in real-world applications, yet most existing approaches rely on large human-annotated datasets and static red-teaming benchmarks that are costly, difficult to scale,…
- Robust Preference Alignment via Directional Neighborhood Consensus
Ruochen Mao, Yuling Shi, Xiaodong Gu, Jiaheng Wei · Oct 23, 2025 · Citations: 0
Automatic Metrics General
To address this challenge, we introduce Robust Preference Selection (RPS), a post-hoc, training-free method by leveraging directional neighborhood consensus.
- Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in Large Language Models
Sanskar Pandey, Ruhaan Chopra, Angkul Puniya, Sohom Pal · Oct 19, 2025 · Citations: 0
Automatic Metrics Medicine
This latent bias, known as sycophancy, manifests as a preference for user agreement over principled reasoning.
- Contextualized Privacy Defense for LLM Agents
Yule Wen, Yanzhe Zhang, Jianxun Lian, Xiaoyuan Yi, Xing Xie · Mar 3, 2026 · Citations: 0
Simulation Env General
LLM agents increasingly act on users' personal information, yet existing privacy defenses remain limited in both design and adaptability.
- CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization
Jinyan Su, Jinpeng Zhou, Claire Cardie, Wen Sun · May 8, 2026 · Citations: 0
General
Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor.
- Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks
Hanjiang Hu, Alexander Robey, Changliu Liu · Feb 28, 2025 · Citations: 0
General
To address this challenge, we propose a safety steering framework grounded in safe control theory, ensuring invariant safety in multi-turn dialogues.
- Towards Automated Community Notes Generation with Large Vision Language Models for Combating Contextual Deception
Jin Ma, Jingwen Yan, Mohammed Aldeen, Ethan Anderson, Taran Kavuru · Mar 23, 2026 · Citations: 0
Automatic Metrics General
However, its reliance on human contributors limits both the timeliness and scalability.
- Hallucination as Commitment Failure: Larger LLMs Misfire Despite Knowing the Answer
Jewon Yeom, Jaewon Sok, Heejun Kim, Seonghyeon Park, Jeongjae Park · May 21, 2026 · Citations: 0
- Personalizing LLMs with Binary Feedback: A Preference-Corrected Optimization Framework
Xilai Ma, Liye Zhao, Weijun Yao, Haibing Di, Wenya Wang · May 11, 2026 · Citations: 0
- EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
Dongxin Guo, Jikun Wu, Siu Ming Yiu · May 10, 2026 · Citations: 0
- Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training
Jasmine Brazilek, Juliana Seawell · Apr 30, 2026 · Citations: 0
- Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations
Mingqian Zheng, Malia Morgan, Liwei Jiang, Carolyn Rose, Maarten Sap · Apr 29, 2026 · Citations: 0
- From Prompt Risk to Response Risk: Paired Analysis of Safety Behavior of Large Language Model
Mengya Hu, Qiong Wei, Sandeep Atluri · Apr 28, 2026 · Citations: 0
- Jailbreaking Frontier Foundation Models Through Intention Deception
Xinhe Wang, Katia Sycara, Yaqi Xie · Apr 27, 2026 · Citations: 0
- Cat-DPO: Category-Adaptive Safety Alignment
Tiankai Yang, Yi Nian, Xinyuan Li, Ruiyao Xu, Kaize Ding · Apr 19, 2026 · Citations: 0
- Robust Reward Modeling for Large Language Models via Causal Decomposition
Yunsheng Lu, Zijiang Yang, Licheng Pan, Zhixuan Chu · Apr 15, 2026 · Citations: 0
- One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu, Massoud Pedram · Apr 14, 2026 · Citations: 0
- One Model for All: Multi-Objective Controllable Language Models
Qiang He, Yucheng Yang, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy · Apr 6, 2026 · Citations: 0
- FINEST: Improving LLM Responses to Sensitive Topics Through Fine-Grained Evaluation
Juhyun Oh, Nayeon Lee, Chani Jung, Jiho Jin, Junho Myung · Mar 4, 2026 · Citations: 0
- Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety
Vamshi Krishna Bonagiri, Ponnurangam Kumaragurum, Khanh Nguyen, Benjamin Plaut · Oct 18, 2025 · Citations: 0
- Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
Jiaying Wu, Zihang Fu, Haonan Wang, Fanxiao Li, Jiafeng Guo · Oct 13, 2025 · Citations: 0
