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20242026
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cs.CL2026

Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts

Bo Wang, Heyan Huang, Yaolin Li +5

Retrieval-augmented generation (RAG) improves language models by grounding generation in external context. However, it can be fragile when the retrieved context conflicts with the…

cs.CL2026

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

Yi-Fan Lu, Xian-Ling Mao, Bo Wang +2

It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event e…

cs.CL2026

Retrieved In-Context Principles from Previous Mistakes

Hao Sun, Yong Jiang, Bo Wang +4

In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Recent advances have attempted to…

cs.CL2026

AdaSwitch: Adaptive Switching between Small and Large Agents for Effective Cloud-Local Collaborative Learning

Hao Sun, Jiayi Wu, Hengyi Cai +6

Recent advancements in large language models (LLMs) have been remarkable. Users face a choice between using cloud-based LLMs for generation quality and deploying local-based LLMs f…

cs.CL2024

Towards Verifiable Text Generation with Evolving Memory and Self-Reflection

Hao Sun, Hengyi Cai, Bo Wang +5

Despite the remarkable ability of large language models (LLMs) in language comprehension and generation, they often suffer from producing factually incorrect information, also know…

cs.CL2024

QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism

Bo Wang, Heyan Huang, Yixin Cao +3

While large language models (LLMs) have made notable advancements in natural language processing, they continue to struggle with processing extensive text. Memory mechanism offers…