7 papers
ReEfBench: Quantifying the Reasoning Efficiency of LLMs
Zhizhang Fu, Yuancheng Gu, Chenkai Hu +2
Test-time scaling has enabled Large Language Models (LLMs) to tackle complex reasoning, yet the limitations of current Chain-of-Thought (CoT) evaluation obscures whether performanc…
JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction
Yuhao Zhan, Yuqing Zhang, Jing Yuan +5
Existing Grammatical Error Correction (GEC) systems suffer from limited reference diversity, leading to underestimated evaluation and restricted model generalization. To address th…
MoKA: Mixture of Kronecker Adapters
Mohammadreza Sadeghi, Mahsa Ghazvini Nejad, MirHamed Jafarzadeh Asl +4
Parameter-efficient fine-tuning (PEFT) is essential for reducing the computational overhead of large language models (LLMs). Low-rank family adapters are commonly used to control t…
Pipelined Decoder for Efficient Context-Aware Text Generation
Zixian Huang, Chenxu Niu, Yu Gu +3
As the basis of generative AI, an autoregressive model requires the generation of a new token depending on all the previously generated tokens, which brings high quality but also r…
USM-VC: Mitigating Timbre Leakage with Universal Semantic Mapping Residual Block for Voice Conversion
Na Li, Chuke Wang, Yu Gu +1
Voice conversion (VC) transforms source speech into a target voice by preserving the content. However, timbre information from the source speaker is inherently embedded in the cont…
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
Peilin Zhou, Bruce Leon, Xiang Ying +13
As large language models (LLMs) evolve into tool-using agents, the ability to browse the web in real-time has become a critical yardstick for measuring their reasoning and retrieva…