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20232026
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cs.CL2025

Preference Orchestrator: Prompt-Aware Multi-Objective Alignment for Large Language Models

Biao Liu, Ning Xu, Junming Yang +1

While Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, aligning these models with varying human preferences…

cs.CL2025

Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective

Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai +8

Large language models (LLMs) have achieved impressive results across a range of natural language processing tasks, but their potential to generate harmful content has raised seriou…

cs.CL2025

Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization

Junming Yang, Ning Xu, Biao Liu +2

Preference optimization is crucial for aligning large language models (LLMs) with human values and intentions. A significant challenge in this process is the distribution mismatch…

cs.CL2024

Negative-Prompt-driven Alignment for Generative Language Model

Shiqi Qiao, Ning Xv, Biao Liu +1

Large language models have achieved remarkable capabilities, but aligning their outputs with human values and preferences remains a significant challenge. Existing alignment method…

cs.CL2024

Progressively Label Enhancement for Large Language Model Alignment

Biao Liu, Ning Xu, Xin Geng

Large Language Models (LLM) alignment aims to prevent models from producing content that misaligns with human expectations, which can lead to ethical and legal concerns. In the las…