Search Results for author: Qiushi Wang

Found 6 papers, 3 papers with code

InsQABench: Benchmarking Chinese Insurance Domain Question Answering with Large Language Models

1 code implementation19 Jan 2025 Jing Ding, Kai Feng, Binbin Lin, Jiarui Cai, Qiushi Wang, Yu Xie, Xiaojin Zhang, Zhongyu Wei, Wei Chen

The application of large language models (LLMs) has achieved remarkable success in various fields, but their effectiveness in specialized domains like the Chinese insurance industry remains underexplored.

Benchmarking Question Answering +1

Distilling Desired Comments for Enhanced Code Review with Large Language Models

no code implementations29 Dec 2024 Yongda Yu, Lei Zhang, Guoping Rong, Haifeng Shen, Jiahao Zhang, Haoxiang Yan, Guohao Shi, Dong Shao, Ruiqi Pan, Yuan Li, Qiushi Wang, Zhao Tian

Human evaluation confirms that both models identify issues more accurately and tend to generate review comments that better describe the issues contained in the code than the base LLMs do.

Dataset Distillation Hallucination

Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models

1 code implementation17 Dec 2024 Yuchen Fan, Yuzhong Hong, Qiushi Wang, Junwei Bao, Hongfei Jiang, Yang song

Alignment, endowing a pre-trained Large language model (LLM) with the ability to follow instructions, is crucial for its real-world applications.

Causal Language Modeling Language Modeling +2

BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation

no code implementations9 Dec 2024 Qiushi Wang, Yuchen Fan, Junwei Bao, Hongfei Jiang, Yang song

In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models.

parameter-efficient fine-tuning

Evaluation of Battery Storage to Provide Virtual Transmission Service

no code implementations13 Sep 2023 Qiushi Wang, Xingpeng Li

An immediate need in the transmission system is to find alternative solutions that improve system operation and defer the need for new transmission lines.

Scheduling

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