Search Results for author: Yutai Hou

Found 19 papers, 12 papers with code

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios

1 code implementation30 Jan 2024 Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.


Automatic Instruction Optimization for Open-source LLM Instruction Tuning

2 code implementations22 Nov 2023 Yilun Liu, Shimin Tao, Xiaofeng Zhao, Ming Zhu, Wenbing Ma, Junhao Zhu, Chang Su, Yutai Hou, Miao Zhang, Min Zhang, Hongxia Ma, Li Zhang, Hao Yang, Yanfei Jiang

However, the manual creation of high-quality instruction datasets is costly, leading to the adoption of automatic generation of instruction pairs by LLMs as a popular alternative in the training of open-source LLMs.

Instruction Following

MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning

no code implementations19 Apr 2023 Bohan Li, Longxu Dou, Yutai Hou, Yunlong Feng, Honglin Mu, Qingfu Zhu, Qinghua Sun, Wanxiang Che

Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.

Data Augmentation Few-Shot Learning +1

Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

no code implementations4 Feb 2023 Bohan Li, Xiao Xu, Xinghao Wang, Yutai Hou, Yunlong Feng, Feng Wang, Xuanliang Zhang, Qingfu Zhu, Wanxiang Che

In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.

Image Augmentation Image Classification +1

MetaPrompting: Learning to Learn Better Prompts

1 code implementation COLING 2022 Yutai Hou, Hongyuan Dong, Xinghao Wang, Bohan Li, Wanxiang Che

Prompting method is regarded as one of the crucial progress for few-shot nature language processing.


Language Anisotropic Cross-Lingual Model Editing

1 code implementation25 May 2022 Yang Xu, Yutai Hou, Wanxiang Che, Min Zhang

On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing.

Model Editing

Data Augmentation Approaches in Natural Language Processing: A Survey

1 code implementation5 Oct 2021 Bohan Li, Yutai Hou, Wanxiang Che

One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data.

Data Augmentation

Discovering Drug-Target Interaction Knowledge from Biomedical Literature

no code implementations27 Sep 2021 Yutai Hou, Yingce Xia, Lijun Wu, Shufang Xie, Yang Fan, Jinhua Zhu, Wanxiang Che, Tao Qin, Tie-Yan Liu

We regard the DTI triplets as a sequence and use a Transformer-based model to directly generate them without using the detailed annotations of entities and relations.

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling

no code implementations Findings (ACL) 2021 Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

However, dialogue language understanding contains two closely related tasks, i. e., intent detection and slot filling, and often benefits from jointly learning the two tasks.

Few-Shot Learning Intent Detection +2

C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

1 code implementation13 Dec 2020 Yutai Hou, Sanyuan Chen, Wanxiang Che, Cheng Chen, Ting Liu

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data.

Data Augmentation slot-filling +2

A Corpus-free State2Seq User Simulator for Task-oriented Dialogue

1 code implementation10 Sep 2019 Yutai Hou, Meng Fang, Wanxiang Che, Ting Liu

The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data.

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

no code implementations20 Jun 2019 Yutai Hou, Zhihan Zhou, Yijia Liu, Ning Wang, Wanxiang Che, Han Liu, Ting Liu

It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism.

Few-Shot Learning named-entity-recognition +3

Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding

1 code implementation COLING 2018 Yutai Hou, Yijia Liu, Wanxiang Che, Ting Liu

In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system.

Text Augmentation

A Statistical Framework for Product Description Generation

no code implementations IJCNLP 2017 Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, Chin-Yew Lin

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes.

Attribute Data-to-Text Generation

Cannot find the paper you are looking for? You can Submit a new open access paper.