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20242026
most citedOnline Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task

14 citations · 21 across the 20 of their papers we have counts for

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6 papers · 1 filter

cs.CL2025

RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior

Junyao Yang, Jianwei Wang, Huiping Zhuang +2

Large Language Models (LLMs) with long chain-of-thought (CoT) capability, termed Reasoning Models, demonstrate superior intricate problem-solving abilities through multi-step long…

cs.CL2025

Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints

Zhengdong Lu, Weikai Lu, Yiling Tao +6

Despite significant advances in Large Language Models (LLMs), planning tasks still present challenges for LLM-based agents. Existing planning methods face two key limitations: heav…

cs.CL2025

SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings

Weikai Lu, Hao Peng, Huiping Zhuang +2

Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text and data of additional modalities…

cs.CL2024★ 4 cited

GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models

Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4

Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…

cs.CL2024

Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge

Weikai Lu, Ziqian Zeng, Jianwei Wang +5

Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense methods have failed to address the…

cs.CL2024★ 1 cited

Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens

Ziqian Zeng, Jiahong Yu, Qianshi Pang +4

Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding pr…