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INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance
Shisong Chen, Qian Zhu, Wenyan Yang +15
Insurance, as a critical component of the global financial system, demands high standards of accuracy and reliability in AI applications. While existing benchmarks evaluate AI capa…
SpindleKV: A Novel KV Cache Reduction Method Balancing Both Shallow and Deep Layers
Zicong Tang, Shi Luohe, Zuchao Li +4
Large Language Models (LLMs) have achieved impressive accomplishments in recent years. However, the increasing memory consumption of KV cache has possessed a significant challenge…
From Misleading Queries to Accurate Answers: A Three-Stage Fine-Tuning Method for LLMs
Guocong Li, Weize Liu, Yihang Wu +4
Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these…
Faster MoE LLM Inference for Extremely Large Models
Haoqi Yang, Luohe Shi, Qiwei Li +5
Sparse Mixture of Experts (MoE) large language models (LLMs) are gradually becoming the mainstream approach for ultra-large-scale models. Existing optimization efforts for MoE mode…
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
Lu Yang, Jiajia Li, En Ci +3
Universal Information Extraction (UIE) has garnered significant attention due to its ability to address model explosion problems effectively. Extractive UIE can achieve strong perf…
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model
Mingni Tang, Jiajia Li, Lu Yang +5
Symbolic music is represented in two distinct forms: two-dimensional, visually intuitive score images, and one-dimensional, standardized text annotation sequences. While large lang…