activity
20202025
most citedLLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

16 citations · 22 across the 11 of their papers we have counts for

collaborators

13 papers

cs.CL2025

SuperWriter: Reflection-Driven Long-Form Generation with Large Language Models

Yuhao Wu, Yushi Bai, Zhiqiang Hu +2

Long-form text generation remains a significant challenge for large language models (LLMs), particularly in maintaining coherence, ensuring logical consistency, and preserving text…

cs.CL2025

LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning

Yuhao Wu, Yushi Bai, Zhiqiang Hu +2

Ultra-long generation by large language models (LLMs) is a widely demanded scenario, yet it remains a significant challenge due to their maximum generation length limit and overall…

cs.CL2024

InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification

Yujia Hu, Zhiqiang Hu, Chun-Wei Seah +1

Large Language Models (LLMs) have demonstrated remarkable proficiency in a wide range of NLP tasks. However, when it comes to authorship verification (AV) tasks, which involve dete…

cs.CL2024

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models

Wenhao Shi, Zhiqiang Hu, Yi Bin +5

Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving. However, existing open-source image instruct…

cs.CL2023

Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification

Chia-Yu Hung, Zhiqiang Hu, Yujia Hu +1

Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection,…

cs.CL2023

Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer

Zhiqiang Hu, Roy Ka-Wei Lee, Nancy F. Chen

Adapting a large language model for multiple-attribute text style transfer via fine-tuning can be challenging due to the significant amount of computational resources and labeled d…