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20242026
most citedScaling Knowledge Graph Construction through Synthetic Data Generation and Distillation

1 citations · 1 across the 2 of their papers we have counts for

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cs.CL2026

YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition

PSBC LLM Team, Huawei LLM Team, Ruihan Long +56

Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates inf…

cs.CL20261 cited

Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation

Prafulla Kumar Choubey, Xin Su, Man Luo +9

Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…

cs.CL2025

DemoShapley: Valuation of Demonstrations for In-Context Learning

Shan Xie, Man Luo, Chadly Daniel Stern +2

Large language models (LLMs) using in-context learning (ICL) excel in many tasks without task-specific fine-tuning. However, demonstration selection and ordering greatly impact ICL…

cs.CL2025

SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs

Xin Su, Man Luo, Kris W Pan +3

Multimodal retrieval augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where external knowledge is needed to an…

cs.CL2024

Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review

Sungduk Yu, Man Luo, Avinash Madasu +2

Peer review is a critical process for ensuring the integrity of published scientific research. Confidence in this process is predicated on the assumption that experts in the releva…