Search Results for author: Xianjun Yang

Found 16 papers, 12 papers with code

A Survey on Detection of LLMs-Generated Content

1 code implementation24 Oct 2023 Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education.

AlpaCare:Instruction-tuned Large Language Models for Medical Application

1 code implementation23 Oct 2023 Xinlu Zhang, Chenxin Tian, Xianjun Yang, Lichang Chen, Zekun Li, Linda Ruth Petzold

However, existing medical instruction-tuned LLMs have been constrained by the limited scope of tasks and instructions available, restricting the efficacy of instruction tuning and adversely affecting performance in the general domain.

Instruction Following Test

Zero-Shot Detection of Machine-Generated Codes

no code implementations8 Oct 2023 Xianjun Yang, Kexun Zhang, Haifeng Chen, Linda Petzold, William Yang Wang, Wei Cheng

We then modify the previous zero-shot text detection method, DetectGPT (Mitchell et al., 2023) by utilizing a surrogate white-box model to estimate the probability of the rightmost tokens, allowing us to identify code snippets generated by language models.

Language Modelling Text Detection

Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models

no code implementations4 Oct 2023 Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, Dahua Lin

This study serves as a clarion call for a collective effort to overhaul and fortify the safety of open-source LLMs against malicious attackers.

Large Language Models Can Be Good Privacy Protection Learners

no code implementations3 Oct 2023 Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng

To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

1 code implementation27 May 2023 Xianjun Yang, Wei Cheng, Yue Wu, Linda Petzold, William Yang Wang, Haifeng Chen

However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs.

Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting

1 code implementation22 May 2023 Xinlu Zhang, Shiyang Li, Xianjun Yang, Chenxin Tian, Yao Qin, Linda Ruth Petzold

Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments.

Decision Making Privacy Preserving

LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation

1 code implementation NeurIPS 2023 Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, William Yang Wang

Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor correlation with human judgments.

Image Generation Image-text matching +1

Dynamic Prompting: A Unified Framework for Prompt Tuning

1 code implementation6 Mar 2023 Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen

Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

no code implementations16 Feb 2023 Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, Wei Cheng

Text summarization has been a crucial problem in natural language processing (NLP) for several decades.

Abstractive Text Summarization

MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures

1 code implementation11 Feb 2023 Xianjun Yang, Stephen Wilson, Linda Petzold

In this paper, we present a novel approach to knowledge extraction and retrieval using Natural Language Processing (NLP) techniques for material science.

Document Classification Retrieval

OASum: Large-Scale Open Domain Aspect-based Summarization

1 code implementation19 Dec 2022 Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, Dong Yu

Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model.

PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text

1 code implementation22 Oct 2022 Xianjun Yang, Ya Zhuo, Julia Zuo, Xinlu Zhang, Stephen Wilson, Linda Petzold

Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction.

Named Entity Recognition Named Entity Recognition (NER) +2

Few-Shot Document-Level Event Argument Extraction

1 code implementation6 Sep 2022 Xianjun Yang, Yujie Lu, Linda Petzold

To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset.

Document-level Event Extraction Event Argument Extraction +1

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