Search Results for author: Yiming Ju

Found 12 papers, 5 papers with code

CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers

1 code implementation COLING 2022 Yiming Ju, Weikang Wang, Yuanzhe Zhang, Suncong Zheng, Kang Liu, Jun Zhao

To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted.

Question Answering

Training Data for Large Language Model

no code implementations12 Nov 2024 Yiming Ju, Huanhuan Ma

In 2022, with the release of ChatGPT, large-scale language models gained widespread attention.

Language Modeling Language Modelling +2

Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data

4 code implementations24 Oct 2024 Shuhao Gu, Jialing Zhang, Siyuan Zhou, Kevin Yu, Zhaohu Xing, Liangdong Wang, Zhou Cao, Jintao Jia, Zhuoyi Zhang, YiXuan Wang, Zhenchong Hu, Bo-Wen Zhang, Jijie Li, Dong Liang, Yingli Zhao, Songjing Wang, Yulong Ao, Yiming Ju, Huanhuan Ma, Xiaotong Li, Haiwen Diao, Yufeng Cui, Xinlong Wang, Yaoqi Liu, Fangxiang Feng, Guang Liu

Despite the availability of several open-source multimodal datasets, limitations in the scale and quality of open-source instruction data hinder the performance of VLMs trained on these datasets, leading to a significant gap compared to models trained on closed-source data.

Question Generation Question-Generation +1

Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging

no code implementations1 Oct 2024 Yiming Ju, Ziyi Ni, Xingrun Xing, Zhixiong Zeng, Hanyu Zhao, Siqi Fan, Zheng Zhang

Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks.

Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency

no code implementations11 Sep 2024 Hanyu Zhao, Li Du, Yiming Ju, ChengWei Wu, Tengfei Pan

With the availability of various instruction datasets, a pivotal challenge is how to effectively select and integrate these instructions to fine-tune large language models (LLMs).

AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies

1 code implementation13 Aug 2024 Bo-Wen Zhang, Liangdong Wang, Ye Yuan, Jijie Li, Shuhao Gu, Mengdi Zhao, Xinya Wu, Guang Liu, ChengWei Wu, Hanyu Zhao, Li Du, Yiming Ju, Quanyue Ma, Yulong Ao, Yingli Zhao, Songhe Zhu, Zhou Cao, Dong Liang, Yonghua Lin, Ming Zhang, Shunfei Wang, Yanxin Zhou, Min Ye, Xuekai Chen, Xinyang Yu, Xiangjun Huang, Jian Yang

In this paper, we present AquilaMoE, a cutting-edge bilingual 8*16B Mixture of Experts (MoE) language model that has 8 experts with 16 billion parameters each and is developed using an innovative training methodology called EfficientScale.

Language Modelling Transfer Learning

KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models

2 code implementations28 Sep 2023 Yiming Ju, Xingrun Xing, Zhixiong Zeng

KLoB can serve as a benchmark for evaluating existing locating methods in language models, and can contributes a method to reassessing the validity of locality hypothesis of factual knowledge.

Unsupervised Text Style Transfer with Deep Generative Models

no code implementations31 Aug 2023 Zhongtao Jiang, Yuanzhe Zhang, Yiming Ju, Kang Liu

We present a general framework for unsupervised text style transfer with deep generative models.

Sentence Style Transfer +2

Generating Hierarchical Explanations on Text Classification Without Connecting Rules

no code implementations24 Oct 2022 Yiming Ju, Yuanzhe Zhang, Kang Liu, Jun Zhao

The opaqueness of deep NLP models has motivated the development of methods for interpreting how deep models predict.

Clustering text-classification +1

Logic Traps in Evaluating Attribution Scores

no code implementations ACL 2022 Yiming Ju, Yuanzhe Zhang, Zhao Yang, Zhongtao Jiang, Kang Liu, Jun Zhao

Meanwhile, since the reasoning process of deep models is inaccessible, researchers design various evaluation methods to demonstrate their arguments.

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