- Moral Preferences of LLMs Under Directed Contextual Influence
Phil Blandfort, Tushar Karayil, Urja Pawar, Robert Graham, Alex McKenzie · Feb 26, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Moral benchmarks for LLMs typically use context-free prompts, implicitly assuming stable preferences.
- DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs
Yanbin Wei, Jiangyue Yan, Chun Kang, Yang Chen, Hua Liu · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
This ``one-size-fits-all'' strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or over-lengthy responses to graph-related queries.
- The ASIR Courage Model: A Phase-Dynamic Framework for Truth Transitions in Human and AI Systems
Hyo Jin Kim · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Although initially formulated for human truth-telling under asymmetric stakes, the same phase-dynamic architecture extends to AI systems operating under policy constraints and alignment filters.
- CCCaption: Dual-Reward Reinforcement Learning for Complete and Correct Image Captioning
Zhijiang Tang, Linhua Wang, Jiaxin Qi, Weihao Jiang, Peng Hou · Feb 25, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Image captioning remains a fundamental task for vision language understanding, yet ground-truth supervision still relies predominantly on human-annotated references.
- Alignment-Weighted DPO: A principled reasoning approach to improve safety alignment
Mengxuan Hu, Vivek V. Datla, Anoop Kumar, Zihan Guan, Sheng Li · Feb 24, 2026 · Citations: 0
Pairwise PreferenceRed Team Automatic Metrics
Recent advances in alignment techniques such as Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Direct Preference Optimization (DPO) have improved the safety of large language models (LLMs).
- Probing Graph Neural Network Activation Patterns Through Graph Topology
Floriano Tori, Lorenzo Bini, Marco Sorbi, Stéphane Marchand-Maillet, Vincent Ginis · Feb 24, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.
- CAMEL: Confidence-Gated Reflection for Reward Modeling
Zirui Zhu, Hailun Xu, Yang Luo, Yong Liu, Kanchan Sarkar · Feb 24, 2026 · Citations: 0
Pairwise PreferenceCritique Edit Automatic Metrics
Reward models play a fundamental role in aligning large language models with human preferences.
- Learning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering
Maryam Amirizaniani, Alireza Salemi, Hamed Zamani · Feb 22, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Long Horizon
Personalization in Question Answering (QA) requires answers that are both accurate and aligned with users' background, preferences, and historical context.
- Simplifying Outcomes of Language Model Component Analyses with ELIA
Aaron Louis Eidt, Nils Feldhus · Feb 20, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
The effectiveness of this approach was empirically validated through a mixed-methods user study, which revealed a clear preference for interactive, explorable interfaces over simpler, static visualizations.
- Differences in Typological Alignment in Language Models' Treatment of Differential Argument Marking
Iskar Deng, Nathalia Xu, Shane Steinert-Threlkeld · Feb 19, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Recent work has shown that language models (LMs) trained on synthetic corpora can exhibit typological preferences that resemble cross-linguistic regularities in human languages, particularly for syntactic phenomena such as word order.
- Modeling Distinct Human Interaction in Web Agents
Faria Huq, Zora Zhiruo Wang, Zhanqiu Guo, Venu Arvind Arangarajan, Tianyue Ou · Feb 19, 2026 · Citations: 0
Pairwise Preference Automatic Metrics Web Browsing
Despite rapid progress in autonomous web agents, human involvement remains essential for shaping preferences and correcting agent behavior as tasks unfold.
- Who can we trust? LLM-as-a-jury for Comparative Assessment
Mengjie Qian, Guangzhi Sun, Mark J. F. Gales, Kate M. Knill · Feb 18, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Large language models (LLMs) are increasingly applied as automatic evaluators for natural language generation assessment often using pairwise comparative judgements.
- Learning Personalized Agents from Human Feedback
Kaiqu Liang, Julia Kruk, Shengyi Qian, Xianjun Yang, Shengjie Bi · Feb 18, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users.
- In Agents We Trust, but Who Do Agents Trust? Latent Source Preferences Steer LLM Generations
Mohammad Aflah Khan, Mahsa Amani, Soumi Das, Bishwamittra Ghosh, Qinyuan Wu · Feb 17, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Agents based on Large Language Models (LLMs) are increasingly being deployed as interfaces to information on online platforms.
- How to Train Your Long-Context Visual Document Model
Austin Veselka · Feb 16, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
We systematically study continued pretraining, supervised finetuning, and preference optimization for 24B and 32B parameter models, backed by extensive LC evaluations and ablations to bridge this gap, and achieve state-of-the-art performanc
- Investigation for Relative Voice Impression Estimation
Kenichi Fujita, Yusuke Ijima · Feb 15, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
The estimation target is a low-dimensional vector derived from subjective evaluations, quantifying the perceptual shift of the second utterance relative to the first along an antonymic axis (e.g., ``Dark--Bright'').
- SCOPE: Selective Conformal Optimized Pairwise LLM Judging
Sher Badshah, Ali Emami, Hassan Sajjad · Feb 13, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Large language models (LLMs) are increasingly used as judges to replace costly human preference labels in pairwise evaluation.
- Reward Modeling from Natural Language Human Feedback
Zongqi Wang, Rui Wang, Yuchuan Wu, Yiyao Yu, Pinyi Zhang · Jan 12, 2026 · Citations: 0
Pairwise PreferenceCritique Edit Automatic Metrics
Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GRMs).
- ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System
Anantha Sharma · Jan 3, 2026 · Citations: 0
Pairwise Preference Automatic Metrics
Detecting distributional drift in high-dimensional data streams presents fundamental challenges: global comparison methods scale poorly, projection-based approaches lose geometric structure, and re-clustering methods suffer from identity in
- Explanation Bias is a Product: Revealing the Hidden Lexical and Position Preferences in Post-Hoc Feature Attribution
Jonathan Kamp, Roos Bakker, Dominique Blok · Dec 11, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
In this work, we delve beyond the superficial inconsistencies between attribution methods, structuring their biases through a model- and method-agnostic framework of three evaluation metrics.
- BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning
Qiusi Zhan, Hyeonjeong Ha, Rui Yang, Sirui Xu, Hanyang Chen · Oct 31, 2025 · Citations: 0
Pairwise Preference Automatic MetricsSimulation Env Long Horizon
Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs.
- Designing and Evaluating Chain-of-Hints for Scientific Question Answering
Anubhav Jangra, Smaranda Muresan · Oct 24, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Using the best performing LLM as the backbone of a quantitative study with 41 participants, we uncover distinct user preferences across hinting strategies, and identify the limitations of automatic evaluation metrics to capture them.
- Robust Preference Alignment via Directional Neighborhood Consensus
Ruochen Mao, Yuling Shi, Xiaodong Gu, Jiaheng Wei · Oct 23, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Aligning large language models with human preferences is critical for creating reliable and controllable AI systems.
- Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty
Yao Xiao, Jung-jae Kim, Roy Ka-wei Lee, Lidong Bing · Oct 7, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Self-play preference optimization has emerged as a prominent paradigm for aligning large language models (LLMs).
- Error Notebook-Guided, Training-Free Part Retrieval in 3D CAD Assemblies via Vision-Language Models
Yunqing Liu, Nan Zhang, Zhiming Tan · Sep 1, 2025 · Citations: 0
Pairwise Preference Automatic Metrics Long Horizon
We additionally contribute a CAD dataset with human preference annotations.
- A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Chalamalasetti Kranti, Jonathan Jordan, Philipp Sadler · Jul 11, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation.
- TaP: A Taxonomy-Guided Framework for Automated and Scalable Preference Data Generation
Renren Jin, Tianhao Shen, Xinwei Wu, Dan Shi, Haoran Sun · Jun 30, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Conducting supervised and preference fine-tuning of large language models (LLMs) requires high-quality datasets to improve their ability to follow instructions and align with human preferences and values.
- Counting trees: A treebank-driven exploration of syntactic variation in speech and writing across languages
Kaja Dobrovoljc · May 28, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Results show that, across both languages, spoken corpora contain fewer and less diverse syntactic structures than their written counterparts, with consistent cross-linguistic preferences for certain structural types across modalities.
- VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models
Yuchen Yan, Jin Jiang, Zhenbang Ren, Yijun Li, Xudong Cai · May 21, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
However, existing reward benchmarks focus on preference comparisons between responses rather than evaluating verification against ground truth references, leaving a critical gap in our ability to evaluate verification systems used in reason
- Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition
Zheng Hui, Xiaokai Wei, Yexi Jiang, Kevin Gao, Chen Wang · Apr 26, 2025 · Citations: 0
Pairwise Preference Automatic Metrics Multi Agent
These domains typically involve fixed content and passive consumption, where user preferences can be matched by genre or theme.
- Evaluating the Diversity and Quality of LLM Generated Content
Alexander Shypula, Shuo Li, Botong Zhang, Vishakh Padmakumar, Kayo Yin · Apr 16, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Recent work suggests that preference-tuning techniques -- such as Reinforcement Learning from Human Feedback (RLHF) methods like PPO and GRPO, as well as alternatives like DPO -- reduce diversity, creating a dilemma given that these models
- Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen · Feb 24, 2025 · Citations: 0
Pairwise Preference Automatic Metrics
Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval.
- Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling
Kaleel Mahmood, Shaoyi Huang · Dec 8, 2024 · Citations: 0
Pairwise Preference Automatic Metrics
One of the key challenges in Transformer architectures is the quadratic complexity of the attention mechanism, which limits the efficient processing of long sequences.
