A Survey on Detection of LLMs-Generated Content
- URL: http://arxiv.org/abs/2310.15654v1
- Date: Tue, 24 Oct 2023 09:10:26 GMT
- Title: A Survey on Detection of LLMs-Generated Content
- Authors: Xianjun Yang, Liangming Pan, Xuandong Zhao, Haifeng Chen, Linda
Petzold, William Yang Wang, Wei Cheng
- Abstract summary: The ability to detect LLMs-generated content has become of paramount importance.
We aim to provide a detailed overview of existing detection strategies and benchmarks.
We also posit the necessity for a multi-faceted approach to defend against various attacks.
- Score: 97.87912800179531
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. As such, the ability to detect LLMs-generated
content has become of paramount importance. We aim to provide a detailed
overview of existing detection strategies and benchmarks, scrutinizing their
differences and identifying key challenges and prospects in the field,
advocating for more adaptable and robust models to enhance detection accuracy.
We also posit the necessity for a multi-faceted approach to defend against
various attacks to counter the rapidly advancing capabilities of LLMs. To the
best of our knowledge, this work is the first comprehensive survey on the
detection in the era of LLMs. We hope it will provide a broad understanding of
the current landscape of LLMs-generated content detection, offering a guiding
reference for researchers and practitioners striving to uphold the integrity of
digital information in an era increasingly dominated by synthetic content. The
relevant papers are summarized and will be consistently updated at
https://github.com/Xianjun-Yang/Awesome_papers_on_LLMs_detection.git.
Related papers
- Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
Large language models (LLMs) generate content that can undermine trust in online discourse.
Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-AI collaboration.
To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
arXiv Detail & Related papers (2024-10-18T08:14:10Z) - From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems.
The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness.
This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
arXiv Detail & Related papers (2024-09-19T02:51:54Z) - Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks [12.893445918647842]
Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns.
This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks.
arXiv Detail & Related papers (2024-09-12T14:42:08Z) - Hide and Seek: Fingerprinting Large Language Models with Evolutionary Learning [0.40964539027092917]
We introduce a novel black-box approach for fingerprinting Large Language Model (LLM) models.
We achieve an impressive 72% accuracy in identifying the correct family of models.
This research opens new avenues for understanding LLM behavior and has significant implications for model attribution, security, and the broader field of AI transparency.
arXiv Detail & Related papers (2024-08-06T00:13:10Z) - Large Language Models for Cyber Security: A Systematic Literature Review [14.924782327303765]
We conduct a comprehensive review of the literature on the application of Large Language Models in cybersecurity (LLM4Security)
We observe that LLMs are being applied to a wide range of cybersecurity tasks, including vulnerability detection, malware analysis, network intrusion detection, and phishing detection.
Third, we identify several promising techniques for adapting LLMs to specific cybersecurity domains, such as fine-tuning, transfer learning, and domain-specific pre-training.
arXiv Detail & Related papers (2024-05-08T02:09:17Z) - Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
This comprehensive survey delves into the recent strides in HS moderation.
We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs)
We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
arXiv Detail & Related papers (2024-01-30T03:51:44Z) - Video Understanding with Large Language Models: A Survey [97.29126722004949]
Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understanding.
The emergent capabilities Vid-LLMs are surprisingly advanced, particularly their ability for open-ended multi-granularity reasoning.
This survey presents a comprehensive study of the tasks, datasets, benchmarks, and evaluation methodologies for Vid-LLMs.
arXiv Detail & Related papers (2023-12-29T01:56:17Z) - A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions [39.36381851190369]
There is an imperative need to develop detectors that can detect LLM-generated text.
This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content.
The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, statistics-based detectors, neural-base detectors, and human-assisted methods.
arXiv Detail & Related papers (2023-10-23T09:01:13Z) - A Comprehensive Overview of Large Language Models [68.22178313875618]
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks.
This article provides an overview of the existing literature on a broad range of LLM-related concepts.
arXiv Detail & Related papers (2023-07-12T20:01:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.