From Text to Source: Results in Detecting Large Language Model-Generated Content
- URL: http://arxiv.org/abs/2309.13322v2
- Date: Wed, 27 Mar 2024 10:50:24 GMT
- Title: From Text to Source: Results in Detecting Large Language Model-Generated Content
- Authors: Wissam Antoun, Benoît Sagot, Djamé Seddah,
- Abstract summary: Large Language Models (LLMs) are celebrated for their ability to generate human-like text.
This paper investigates "Cross-Model Detection," by evaluating whether a classifier trained to distinguish between source LLM-generated and human-written text can also detect text from a target LLM without further training.
The research also explores Model Attribution, encompassing source model identification, model family, and model size classification, in addition to quantization and watermarking detection.
- Score: 17.306542392779445
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The widespread use of Large Language Models (LLMs), celebrated for their ability to generate human-like text, has raised concerns about misinformation and ethical implications. Addressing these concerns necessitates the development of robust methods to detect and attribute text generated by LLMs. This paper investigates "Cross-Model Detection," by evaluating whether a classifier trained to distinguish between source LLM-generated and human-written text can also detect text from a target LLM without further training. The study comprehensively explores various LLM sizes and families, and assesses the impact of conversational fine-tuning techniques, quantization, and watermarking on classifier generalization. The research also explores Model Attribution, encompassing source model identification, model family, and model size classification, in addition to quantization and watermarking detection. Our results reveal several key findings: a clear inverse relationship between classifier effectiveness and model size, with larger LLMs being more challenging to detect, especially when the classifier is trained on data from smaller models. Training on data from similarly sized LLMs can improve detection performance from larger models but may lead to decreased performance when dealing with smaller models. Additionally, model attribution experiments show promising results in identifying source models and model families, highlighting detectable signatures in LLM-generated text, with particularly remarkable outcomes in watermarking detection, while no detectable signatures of quantization were observed. Overall, our study contributes valuable insights into the interplay of model size, family, and training data in LLM detection and attribution.
Related papers
- Robust Detection of LLM-Generated Text: A Comparative Analysis [0.276240219662896]
Large language models can be widely integrated into many aspects of life, and their output can quickly fill all network resources.
It becomes increasingly important to develop powerful detectors for the generated text.
This detector is essential to prevent the potential misuse of these technologies and to protect areas such as social media from the negative effects.
arXiv Detail & Related papers (2024-11-09T18:27:15Z) - 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) - 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) - Catching Chameleons: Detecting Evolving Disinformation Generated using Large Language Models [16.408611714514976]
We propose DELD (Detecting Evolving LLM-generated Disinformation), a parameter-efficient approach that jointly leverages the general fact-checking capabilities of pre-trained language models.
Our experiments show that textitDELD significantly outperforms state-of-the-art methods.
arXiv Detail & Related papers (2024-06-26T00:21:39Z) - Self-training Large Language Models through Knowledge Detection [26.831873737733737]
Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks.
This paper explores a self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples.
Empirical evaluations demonstrate significant improvements in reducing hallucination in generation across multiple subjects.
arXiv Detail & Related papers (2024-06-17T07:25:09Z) - DALD: Improving Logits-based Detector without Logits from Black-box LLMs [56.234109491884126]
Large Language Models (LLMs) have revolutionized text generation, producing outputs that closely mimic human writing.
We present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection.
DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations.
arXiv Detail & Related papers (2024-06-07T19:38:05Z) - PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
PoLLMgraph is a model-based white-box detection and forecasting approach for large language models.
We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics.
Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
arXiv Detail & Related papers (2024-04-06T20:02:20Z) - Evolving Knowledge Distillation with Large Language Models and Active
Learning [46.85430680828938]
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks.
Previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data.
We propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models.
arXiv Detail & Related papers (2024-03-11T03:55:24Z) - Characterizing Truthfulness in Large Language Model Generations with
Local Intrinsic Dimension [63.330262740414646]
We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs)
We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
arXiv Detail & Related papers (2024-02-28T04:56:21Z) - To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis [50.31589712761807]
Large language models (LLMs) are notoriously token-hungry during pre-training, and high-quality text data on the web is approaching its scaling limit for LLMs.
We investigate the consequences of repeating pre-training data, revealing that the model is susceptible to overfitting.
Second, we examine the key factors contributing to multi-epoch degradation, finding that significant factors include dataset size, model parameters, and training objectives.
arXiv Detail & Related papers (2023-05-22T17:02:15Z) - Large Language Models Are Latent Variable Models: Explaining and Finding
Good Demonstrations for In-Context Learning [104.58874584354787]
In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning.
This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
arXiv Detail & Related papers (2023-01-27T18:59:01Z)
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.