5 citations · 5 across the 8 of their papers we have counts for
8 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…
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…
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…
Towards Differentiable Multilevel Optimization: A Gradient-Based Approach
Yuntian Gu, Xuzheng Chen
Multilevel optimization has gained renewed interest in machine learning due to its promise in applications such as hyperparameter tuning and continual learning. However, existing m…
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Junyang Wang, Yuhang Wang, Guohai Xu +8
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to…