- BiasFreeBench: a Benchmark for Mitigating Bias in Large Language Model Responses
Xin Xu, Xunzhi He, Churan Zhi, Ruizhe Chen, Julian McAuley · Sep 30, 2025 · Citations: 0
Moreover, their evaluations are mostly based on the comparison between LLMs' probabilities of biased and unbiased contexts, which ignores the gap between such evaluations and real-world use cases where users interact with LLMs by reading…
- PrefDisco: Benchmarking Proactive Personalized Reasoning
Shuyue Stella Li, Avinandan Bose, Faeze Brahman, Simon Shaolei Du, Pang Wei Koh · Sep 30, 2025 · Citations: 0
Pairwise PreferenceRubric Rating
We introduce PrefDisco, an evaluation methodology that transforms static benchmarks into interactive personalization tasks using psychologically-grounded personas with sparse, context-dependent preferences, and define PrefAlign as a…
- MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages
Chenxi Whitehouse, Sebastian Ruder, Tony Lin, Oksana Kurylo, Haruka Takagi · Sep 30, 2025 · Citations: 0
Pairwise PreferenceRubric Rating
To address this, we introduce MENLO, a framework that operationalizes the evaluation of native-like response quality based on audience design-inspired mechanisms.
- Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Shuai Shao, Qihan Ren, Chen Qian, Boyi Wei, Dadi Guo · Sep 30, 2025 · Citations: 0
Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities.
- EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing
Keming Wu, Sicong Jiang, Max Ku, Ping Nie, Minghao Liu · Sep 30, 2025 · Citations: 0
Pairwise Preference
To address this critical bottleneck, we built EditReward, trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs.
- Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts
Hanwen Du, Yuxin Dong, Xia Ning · Sep 30, 2025 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- ProfVLM: A Lightweight Video-Language Model for Multi-View Proficiency Estimation
Edoardo Bianchi, Jacopo Staiano, Antonio Liotta · Sep 30, 2025 · Citations: 0
Critique Edit
ProfVLM leverages conditional language generation to provide actionable insights along with quantitative evaluation scores.
- LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts
Yuan Zhuang, Yi Shen, Yuexin Bian, Qing Su, Shihao Ji · Sep 30, 2025 · Citations: 0
Extensive experiments on the Qwen3-1.7B and Llama-3.2-3B models show that LD-MoLE achieves the highest average scores compared to state-of-the-art baselines, across a diverse set of benchmarks.
