6 papers
DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection
Junchao Wu, Yefeng Liu, Chenyu Zhu +8
The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misuse. Despite the impressive pe…
GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models
Zhiwen Ruan, Yichao Du, Jianjie Zheng +6
A promising paradigm for adapting instruction-tuned language models is to learn task-specific updates on a pretrained base model and subsequently merge them into the instruction-tu…
Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling
Fan Jiang, Yu Zhao, Chenyang Lyu +5
We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only around 5\% of the total par…
UMEM: Unified Memory Extraction and Management Framework for Generalizable Memory
Yongshi Ye, Hui Jiang, Feihu Jiang +7
Self-evolving memory serves as the trainable parameters for Large Language Models (LLMs)-based agents, where extraction (distilling insights from experience) and management (updati…
LongSpeech: A Scalable Benchmark for Transcription, Translation and Understanding in Long Speech
Fei Yang, Xuanfan Ni, Renyi Yang +7
Recent advances in audio-language models have demonstrated remarkable success on short, segment-level speech tasks. However, real-world applications such as meeting transcription,…
Marco-ASR: A Principled and Metric-Driven Framework for Fine-Tuning Large-Scale ASR Models for Domain Adaptation
Xuanfan Ni, Fei Yang, Fengping Tian +6
Automatic Speech Recognition (ASR) models have achieved remarkable accuracy in general settings, yet their performance often degrades in domain-specific applications due to data mi…