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cs.CL2025

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

Zhijun Chen, Zeyu Ji, Qianren Mao +13

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…

cs.CL2025

Pretraining Large Language Models with NVFP4

NVIDIA, Felix Abecassis, Anjulie Agrusa +87

Large Language Models (LLMs) today are powerful problem solvers across many domains, and they continue to get stronger as they scale in model size, training set size, and training…

cs.CL2025

Making Language Model a Hierarchical Classifier

Yihong Wang, Zhonglin Jiang, Ningyuan Xi +8

Decoder-only language models, such as GPT and LLaMA, generally decode on the last layer. Motivated by human's hierarchical thinking capability, we propose that a hierarchical decod…

cs.CL2025

MemOS: A Memory OS for AI System

Zhiyu Li, Chenyang Xi, Chunyu Li +36

Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…

cs.CL2024

: Language Modeling with Explicit Memory

Hongkang Yang, Zehao Lin, Wenjin Wang +13

The training and inference of large language models (LLMs) are together a costly process that transports knowledge from raw data to meaningful computation. Inspired by the memory h…

cs.CL2024

emrQA-msquad: A Medical Dataset Structured with the SQuAD V2.0 Framework, Enriched with emrQA Medical Information

Jimenez Eladio, Hao Wu

Machine Reading Comprehension (MRC) holds a pivotal role in shaping Medical Question Answering Systems (QAS) and transforming the landscape of accessing and applying medical inform…