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20242026
most citedExplainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey

11 citations · 18 across the 22 of their papers we have counts for

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8 papers · 1 filter

cs.CL2026

MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights

Yilun Liu, Miao Zhang, Shimin Tao +9

Multilingual and multicultural benchmarks now cover dozens of languages and model families, but the resulting score landscapes remain metric-rich and insight-poor, necessitating fi…

cs.CL2026

AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

Zhaopeng Feng, Liangcai Su, Zhen Zhang +16

As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…

cs.CL2026

ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging

Junyao Yang, Chen Qian, Dongrui Liu +3

Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilit…

cs.CL2025

LED-Merging: Mitigating Safety-Utility Conflicts in Model Merging with Location-Election-Disjoint

Qianli Ma, Dongrui Liu, Qian Chen +2

Fine-tuning pre-trained Large Language Models (LLMs) for specialized tasks incurs substantial computational and data costs. While model merging offers a training-free solution to i…

cs.CL2024★ 11 cited

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey

Yunkai Dang, Kaichen Huang, Jiahao Huo +11

The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in…

cs.CL2024★ 2 cited

REEF: Representation Encoding Fingerprints for Large Language Models

Jie Zhang, Dongrui Liu, Chen Qian +4

Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefor…

cs.CL2024★ 1 cited

The Better Angels of Machine Personality: How Personality Relates to LLM Safety

Jie Zhang, Dongrui Liu, Chen Qian +4

Personality psychologists have analyzed the relationship between personality and safety behaviors in human society. Although Large Language Models (LLMs) demonstrate personality tr…

cs.CL2024

Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models

Chen Qian, Jie Zhang, Wei Yao +5

Ensuring the trustworthiness of large language models (LLMs) is crucial. Most studies concentrate on fully pre-trained LLMs to better understand and improve LLMs' trustworthiness.…