Search Results for author: Linyi Yang

Found 25 papers, 16 papers with code

Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature

1 code implementation8 Oct 2023 Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, Linyi Yang, Yue Zhang

Large language models (LLMs) have shown the ability to produce fluent and cogent content, presenting both productivity opportunities and societal risks.

A Survey on Evaluation of Large Language Models

1 code implementation6 Jul 2023 Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.


Masked conditional variational autoencoders for chromosome straightening

no code implementations25 Jun 2023 Jingxiong Li, Sunyi Zheng, Zhongyi Shui, Shichuan Zhang, Linyi Yang, Yuxuan Sun, Yunlong Zhang, Honglin Li, Yuanxin Ye, Peter M. A. van Ooijen, Kang Li, Lin Yang

This yields a non-trivial reconstruction task, allowing the model to effectively preserve chromosome banding patterns and structure details in the reconstructed results.

PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization

2 code implementations8 Jun 2023 Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang

To ensure the reliability of PandaLM, we collect a diverse human-annotated test dataset, where all contexts are generated by humans and labels are aligned with human preferences.

Language Modelling Large Language Model

PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

1 code implementation7 Jun 2023 Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, Xing Xie

The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts.

Machine Translation Math +3

Out-of-Distribution Generalization in Text Classification: Past, Present, and Future

no code implementations23 May 2023 Linyi Yang, Yaoxiao Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang

Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution.

Out-of-Distribution Generalization text-classification +1

Deepfake Text Detection in the Wild

1 code implementation22 May 2023 Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang

In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources.

Face Swapping Story Generation +1

Measuring Consistency in Text-based Financial Forecasting Models

1 code implementation15 May 2023 Linyi Yang, Yingpeng Ma, Yue Zhang

Using FinTrust, we show that the consistency of state-of-the-art NLP models for financial forecasting is poor.

Learning to Generalize for Cross-domain QA

1 code implementation14 May 2023 Yingjie Niu, Linyi Yang, Ruihai Dong, Yue Zhang

Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models.

Data Augmentation Domain Generalization +1

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

1 code implementation22 Feb 2023 Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, Xing Xie

In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.

Adversarial Robustness Chatbot +1

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

1 code implementation15 Nov 2022 Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.

Natural Language Understanding Out-of-Distribution Generalization

Pre-Training a Graph Recurrent Network for Language Representation

1 code implementation8 Sep 2022 Yile Wang, Linyi Yang, Zhiyang Teng, Ming Zhou, Yue Zhang

Transformer-based pre-trained models have gained much advance in recent years, becoming one of the most important backbones in natural language processing.

Language Modelling text-classification +1

Towards Fine-grained Causal Reasoning and QA

1 code implementation15 Apr 2022 Linyi Yang, Zhen Wang, Yuxiang Wu, Jie Yang, Yue Zhang

Understanding causality is key to the success of NLP applications, especially in high-stakes domains.

Question Answering

Challenges for Open-domain Targeted Sentiment Analysis

no code implementations14 Apr 2022 Yun Luo, Hongjie Cai, Linyi Yang, Yanxia Qin, Rui Xia, Yue Zhang

Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6, 013 human-labeled data to extend the data domains in topics of interest and document level.

Sentiment Analysis

A Rationale-Centric Framework for Human-in-the-loop Machine Learning

1 code implementation ACL 2022 Jinghui Lu, Linyi Yang, Brian Mac Namee, Yue Zhang

We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in few-shot learning scenarios.

BIG-bench Machine Learning Few-Shot Learning

NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting

no code implementations5 Jan 2022 Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, Barry Smyth

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail.

Fact Check: Analyzing Financial Events from Multilingual News Sources

no code implementations29 Jun 2021 Linyi Yang, Tin Lok James Ng, Barry Smyth, Ruihai Dong

The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis.


Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis

1 code implementation ACL 2021 Linyi Yang, Jiazheng Li, Pádraig Cunningham, Yue Zhang, Barry Smyth, Ruihai Dong

While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.

counterfactual Data Augmentation +1

Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

no code implementations COLING 2020 Linyi Yang, Eoin M. Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, Ruihai Dong

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence.

counterfactual Explainable Artificial Intelligence (XAI) +3

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