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20242026
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cs.CL2026

Beyond "I cannot fulfill this request": Alleviating Rigid Rejection in LLMs via Label Enhancement

Ying Zhang, Congyu Qiao, Xin Geng +1

Large Language Models (LLMs) rely on safety alignment to obey safe requests while refusing harmful ones. However, traditional refusal mechanisms often lead to "rigid rejection," wh…

cs.CL2026

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models

Boyu Shi, YiCheng Jiang, Chang Liu +3

Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SLMs) from scratch is computat…

cs.CL2026

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.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.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…