Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
Fuente:
arXiv
Saved in:
| Main Authors: | Kou, Zhiqiang, Chen, Junyang, Cai, Xin-Qiang, Xie, Ming-Kun, Liu, Biao, Wang, Changwei, Feng, Lei, Jia, Yuheng, Niu, Gang, Sugiyama, Masashi, Geng, Xin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
by: Kou, Zhiqiang, et al.
Published: (2026)
by: Kou, Zhiqiang, et al.
Published: (2026)
Rethinking Consistent Multi-Label Classification Under Inexact Supervision
by: Wang, Wei, et al.
Published: (2025)
by: Wang, Wei, et al.
Published: (2025)
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
by: Kou, Zhiqiang, et al.
Published: (2025)
by: Kou, Zhiqiang, et al.
Published: (2025)
What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
by: Xie, Ming-Kun, et al.
Published: (2025)
by: Xie, Ming-Kun, et al.
Published: (2025)
Inaccurate Label Distribution Learning with Dependency Noise
by: Kou, Zhiqiang, et al.
Published: (2024)
by: Kou, Zhiqiang, et al.
Published: (2024)
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
by: Kou, Zhiqiang, et al.
Published: (2026)
by: Kou, Zhiqiang, et al.
Published: (2026)
Are Multimodal Large Language Models Good Annotators for Image Tagging?
by: Xie, Ming-Kun, et al.
Published: (2026)
by: Xie, Ming-Kun, et al.
Published: (2026)
Multi-Label Knowledge Distillation
by: Yang, Penghui, et al.
Published: (2023)
by: Yang, Penghui, et al.
Published: (2023)
VI-CuRL: Stabilizing Verifier-Independent RL Reasoning via Confidence-Guided Variance Reduction
by: Cai, Xin-Qiang, et al.
Published: (2026)
by: Cai, Xin-Qiang, et al.
Published: (2026)
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
by: Xie, Ming-Kun, et al.
Published: (2024)
by: Xie, Ming-Kun, et al.
Published: (2024)
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
by: Xiao, Jia-Hao, et al.
Published: (2024)
by: Xiao, Jia-Hao, et al.
Published: (2024)
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
by: Cai, Xin-Qiang, et al.
Published: (2025)
by: Cai, Xin-Qiang, et al.
Published: (2025)
Towards Better Performance in Incomplete LDL: Addressing Data Imbalance
by: Kou, Zhiqiang, et al.
Published: (2024)
by: Kou, Zhiqiang, et al.
Published: (2024)
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
by: Wu, Junxiang, et al.
Published: (2026)
by: Wu, Junxiang, et al.
Published: (2026)
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)
by: Zhang, Jingfeng, et al.
Published: (2023)
Realistic Evaluation of Deep Partial-Label Learning Algorithms
by: Wang, Wei, et al.
Published: (2025)
by: Wang, Wei, et al.
Published: (2025)
Offline Reinforcement Learning with Domain-Unlabeled Data
by: Nishimori, Soichiro, et al.
Published: (2024)
by: Nishimori, Soichiro, et al.
Published: (2024)
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
by: Wang, Wei, et al.
Published: (2023)
by: Wang, Wei, et al.
Published: (2023)
Decoupling the Class Label and the Target Concept in Machine Unlearning
by: Zhu, Jianing, et al.
Published: (2024)
by: Zhu, Jianing, et al.
Published: (2024)
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
by: Xie, Zeke, et al.
Published: (2020)
by: Xie, Zeke, et al.
Published: (2020)
Progressively Label Enhancement for Large Language Model Alignment
by: Liu, Biao, et al.
Published: (2024)
by: Liu, Biao, et al.
Published: (2024)
Soft-Label Integration for Robust Toxicity Classification
by: Cheng, Zelei, et al.
Published: (2024)
by: Cheng, Zelei, et al.
Published: (2024)
Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms
by: Wang, Wei, et al.
Published: (2025)
by: Wang, Wei, et al.
Published: (2025)
From Coefficients to Directions: Rethinking Model Merging with Directional Alignment
by: Chen, Zhikang, et al.
Published: (2025)
by: Chen, Zhikang, et al.
Published: (2025)
Can Class-Priors Help Single-Positive Multi-Label Learning?
by: Liu, Biao, et al.
Published: (2023)
by: Liu, Biao, et al.
Published: (2023)
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
by: Mai, Tan-Ha, et al.
Published: (2026)
by: Mai, Tan-Ha, et al.
Published: (2026)
Scalable Label Distribution Learning for Multi-Label Classification
by: Zhao, Xingyu, et al.
Published: (2023)
by: Zhao, Xingyu, et al.
Published: (2023)
Partial Label Clustering
by: Xie, Yutong, et al.
Published: (2025)
by: Xie, Yutong, et al.
Published: (2025)
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation
by: Xu, Jie, et al.
Published: (2025)
by: Xu, Jie, et al.
Published: (2025)
Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection
by: Chen, Yongqiang, et al.
Published: (2025)
by: Chen, Yongqiang, et al.
Published: (2025)
Reinforcement Learning from Bagged Reward
by: Tang, Yuting, et al.
Published: (2024)
by: Tang, Yuting, et al.
Published: (2024)
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
by: Guo, Shunxin, et al.
Published: (2024)
by: Guo, Shunxin, et al.
Published: (2024)
Generating Chain-of-Thoughts with a Pairwise-Comparison Approach to Searching for the Most Promising Intermediate Thought
by: Zhang, Zhen-Yu, et al.
Published: (2024)
by: Zhang, Zhen-Yu, et al.
Published: (2024)
In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement
by: Zhang, Zhen-Yu, et al.
Published: (2024)
by: Zhang, Zhen-Yu, et al.
Published: (2024)
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning
by: Tang, Yuting, et al.
Published: (2024)
by: Tang, Yuting, et al.
Published: (2024)
UC-MOA: Utility-Conditioned Multi-Objective Alignment for Distributional Pareto-Optimality
by: Cheng, Zelei, et al.
Published: (2025)
by: Cheng, Zelei, et al.
Published: (2025)
Weak-to-Strong Diffusion with Reflection
by: Bai, Lichen, et al.
Published: (2025)
by: Bai, Lichen, et al.
Published: (2025)
Rethinking Data Mixing from the Perspective of Large Language Models
by: Xu, Yuanjian, et al.
Published: (2026)
by: Xu, Yuanjian, et al.
Published: (2026)
Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
by: Wang, Bing, et al.
Published: (2026)
by: Wang, Bing, et al.
Published: (2026)
What Is Preference Optimization Doing, and Why?
by: Wang, Yue, et al.
Published: (2025)
by: Wang, Yue, et al.
Published: (2025)
Similar Items
-
Positive-Unlabeled Reinforcement Learning Distillation for On-Premise Small Models
by: Kou, Zhiqiang, et al.
Published: (2026) -
Rethinking Consistent Multi-Label Classification Under Inexact Supervision
by: Wang, Wei, et al.
Published: (2025) -
Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
by: Kou, Zhiqiang, et al.
Published: (2025) -
What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
by: Xie, Ming-Kun, et al.
Published: (2025) -
Inaccurate Label Distribution Learning with Dependency Noise
by: Kou, Zhiqiang, et al.
Published: (2024)