- Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization
Qiyao Ma, Dechen Gao, Rui Cai, Boqi Zhao, Hanchu Zhou · Apr 8, 2026 · Citations: 0
Pairwise PreferenceRubric Rating Human EvalAutomatic Metrics
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.
- From Consensus to Split Decisions: ABC-Stratified Sentiment in Holocaust Oral Histories
Daban Q. Jaff · Mar 30, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
After assembling model outputs, we introduce an agreement-based stability taxonomy (ABC) to stratify inter-model output stability.
- HyperMem: Hypergraph Memory for Long-Term Conversations
Juwei Yue, Chuanrui Hu, Jiawei Sheng, Zuyi Zhou, Wenyuan Zhang · Apr 9, 2026 · Citations: 0
Pairwise Preference Llm As JudgeAutomatic Metrics
Long-term memory is essential for conversational agents to maintain coherence, track persistent tasks, and provide personalized interactions across extended dialogues.
- Signals: Trajectory Sampling and Triage for Agentic Interactions
Shuguang Chen, Adil Hafeez, Salman Paracha · Apr 1, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Long Horizon
We propose a lightweight, signal-based framework for triaging agentic interaction trajectories.
- Aligning Multimodal Sequential Recommendations via Robust Direct Preference Optimization with Sparse MoE
Hejin Huang, Jusheng Zhang, Kaitong Cai, Jian Wang, Rong Pan · Mar 31, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Preference-based alignment objectives have been widely adopted, from RLHF-style pairwise learning in large language models to emerging applications in recommender systems.
- MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control
Yuchi Wang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang · Apr 7, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Experiments on the MMEB-V2 benchmark demonstrate that our model achieves a score of 71.2 with only 4B parameters, establishing a new state-of-the-art while significantly reducing reasoning overhead and inference latency.
- Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu · Apr 2, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
However, current reranking models are typically optimized on static human annotated relevance labels in isolation, decoupled from the downstream generation process.
- 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
Pairwise Preference Automatic Metrics
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.
- MemRerank: Preference Memory for Personalized Product Reranking
Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yu Gong · Mar 31, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.
- Routing Sensitivity Without Controllability: A Diagnostic Study of Fairness in MoE Language Models
Junhyeok Lee, Kyu Sung Choi · Mar 28, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
FARE reveals that routing-level preference shifts are either unachievable (Mixtral, Qwen1.5, Qwen3), statistically non-robust (DeepSeekMoE), or accompanied by substantial utility cost (OLMoE, -4.4%p CrowS-Pairs at -6.3%p TQA).
- Self-Debias: Self-correcting for Debiasing Large Language Models
Xuan Feng, Shuai Zhao, Luwei Xiao, Tianlong Gu, Bo An · Apr 9, 2026 · Citations: 0
Pairwise Preference Long Horizon
Unlike standard preference optimization which applies broad penalties, Self-Debias employs a fine-grained trajectory-level objective subject to dynamic debiasing constraints.
- Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning
Shiwan Zhao, Zhihu Wang, Xuyang Zhao, Jiaming Zhou, Caiyue Xu · Apr 9, 2026 · Citations: 0
Pairwise Preference Long Horizon
Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines.
- Label Effects: Shared Heuristic Reliance in Trust Assessment by Humans and LLM-as-a-Judge
Xin Sun, Di Wu, Sijing Qin, Isao Echizen, Abdallah El Ali · Apr 7, 2026 · Citations: 0
Pairwise Preference Llm As Judge
Large language models (LLMs) are increasingly used as automated evaluators (LLM-as-a-Judge).
- JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
Aichen Cai, Anmeng Zhang, Anyu Li, Bo Zhang, Bohua Cai · Apr 3, 2026 · Citations: 0
Pairwise Preference
JoyAI-LLM Flash is pretrained on a massive corpus of 20 trillion tokens and further optimized through a rigorous post-training pipeline, including supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and large-scale…
- Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization
Weixu Zhang, Ye Yuan, Changjiang Han, Yuxing Tian, Zipeng Sun · Apr 24, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
In this work, we adopt a mechanistic interpretability perspective and hypothesize the existence of a sparse set of Preference Heads, attention heads that encode user specific stylistic and topical preferences and exert a causal influence on…
- PLOT: Enhancing Preference Learning via Optimal Transport
Liang Zhu, Yuelin Bai, Xiankun Ren, Jiaxi Yang, Lei Zhang · Apr 2, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Preference learning in Large Language Models (LLMs) has advanced significantly, yet existing methods remain limited by modest performance gains, high computational costs, hyperparameter sensitivity, and insufficient modeling of global…
- ThinknCheck: Grounded Claim Verification with Compact, Reasoning-Driven, and Interpretable Models
Delip Rao, Feijiang Han, Chris Callison-Burch · Apr 2, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
By contrast, zero-shot chain-of-thought on the base Gemma3-1B harms accuracy relative to direct answers, and preference optimization with a simple format+accuracy reward underperforms supervised reasoning.
- Controlling Distributional Bias in Multi-Round LLM Generation via KL-Optimized Fine-Tuning
Yanbei Jiang, Amr Keleg, Ryandito Diandaru, Jey Han Lau, Lea Frermann · Apr 7, 2026 · Citations: 0
Pairwise Preference
Our empirical analysis reveals that off-the-shelf LLMs and standard alignment techniques, including prompt engineering and Direct Preference Optimization, fail to reliably control output distributions.
- TSUBASA: Improving Long-Horizon Personalization via Evolving Memory and Self-Learning with Context Distillation
Xinliang Frederick Zhang, Lu Wang · Apr 9, 2026 · Citations: 0
Pairwise Preference Long Horizon
Personalized large language models (PLLMs) have garnered significant attention for their ability to align outputs with individual's needs and preferences.
- Discourse Coherence and Response-Guided Context Rewriting for Multi-Party Dialogue Generation
Zhiyu Cao, Peifeng Li, Qiaoming Zhu · Apr 8, 2026 · Citations: 0
Pairwise Preference
Specifically, DRCR employs two complementary feedback signals, discourse coherence and response quality, to construct preference data for both context rewriting and response generation.
- Evaluating Learner Representations for Differentiation Prior to Instructional Outcomes
Junsoo Park, Youssef Medhat, Htet Phyo Wai, Ploy Thajchayapong, Ashok K. Goel · Apr 7, 2026 · Citations: 0
Pairwise Preference
We introduce distinctiveness, a representation-level measure that evaluates how each learner differs from others in the cohort using pairwise distances, without requiring clustering, labels, or task-specific evaluation.
- Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once
Harnoor Dhingra · Apr 2, 2026 · Citations: 0
Pairwise Preference
We organize tasks into four normative contexts: epistemic (factuality), interactional (user utility), societal (representation), and safety (robustness).
- Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems
Pegah Ramezani, Thomas Kinfe, Andreas Maier, Achim Schilling, Patrick Krauss · Mar 31, 2026 · Citations: 0
Pairwise Preference
The present study tests these predictions in human neural activity using electroencephalography (EEG).
- MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
Kexin Huang, Liwei Fan, Botian Jiang, Yaozhou Jiang, Qian Tu · Mar 30, 2026 · Citations: 0
Pairwise Preference
Such controllable voice creation benefits a wide range of downstream applications-including storytelling, game dubbing, role-play agents, and conversational assistants, making it a significant task for modern Text-to-Speech models.
- Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Addison J. Wu, Ryan Liu, Shuyue Stella Li, Yulia Tsvetkov, Thomas L. Griffiths · Apr 9, 2026 · Citations: 0
Pairwise Preference
Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning.
- Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
Zhiyu Cao, Peifeng Li, Qiaoming Zhu · Apr 8, 2026 · Citations: 0
Pairwise Preference
To address this issue, we propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR).
- DEFT: Distribution-guided Efficient Fine-Tuning for Human Alignment
Liang Zhu, Feiteng Fang, Yuelin Bai, Longze Chen, Zhexiang Zhang · Apr 2, 2026 · Citations: 0
Pairwise Preference
Reinforcement Learning from Human Feedback (RLHF), using algorithms like Proximal Policy Optimization (PPO), aligns Large Language Models (LLMs) with human values but is costly and unstable.
- Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
Gyuseok Lee, Wonbin Kweon, Zhenrui Yue, SeongKu Kang, Jiawei Han · Apr 1, 2026 · Citations: 0
Pairwise Preference
We introduce Variational Reward Factorization (VRF), an uncertainty-aware framework that represents each user's preferences as a variational distribution in a shared preference space.
- From Baselines to Preferences: A Comparative Study of LoRA/QLoRA and Preference Optimization for Mental Health Text Classification
Mihael Arcan · Apr 1, 2026 · Citations: 0
Pairwise Preference
We first establish classical and encoder references, then examine parameter-efficient supervised fine-tuning with LoRA/QLoRA under multiple objective and optimization settings, and finally evaluate preference-based optimization with DPO,…