SPOT: Text Source Prediction from Originality Score Thresholding
- URL: http://arxiv.org/abs/2405.20505v1
- Date: Thu, 30 May 2024 21:51:01 GMT
- Title: SPOT: Text Source Prediction from Originality Score Thresholding
- Authors: Edouard Yvinec, Gabriel Kasser,
- Abstract summary: countermeasures aim at detecting misinformation, usually involve domain specific models trained to recognize the relevance of any information.
Instead of evaluating the validity of the information, we propose to investigate LLM generated text from the perspective of trust.
- Score: 6.790905400046194
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The wide acceptance of large language models (LLMs) has unlocked new applications and social risks. Popular countermeasures aim at detecting misinformation, usually involve domain specific models trained to recognize the relevance of any information. Instead of evaluating the validity of the information, we propose to investigate LLM generated text from the perspective of trust. In this study, we define trust as the ability to know if an input text was generated by a LLM or a human. To do so, we design SPOT, an efficient method, that classifies the source of any, standalone, text input based on originality score. This score is derived from the prediction of a given LLM to detect other LLMs. We empirically demonstrate the robustness of the method to the architecture, training data, evaluation data, task and compression of modern LLMs.
Related papers
- SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation [55.61004653386632]
Large Language Models (LLMs) often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context.
This paper introduces a novel self-supervised method for generating a training set of unfaithful samples.
We then refine the model using a training process that encourages the generation of grounded outputs over unfaithful ones.
arXiv Detail & Related papers (2025-02-19T12:31:58Z) - Idiosyncrasies in Large Language Models [54.26923012617675]
We unveil and study idiosyncrasies in Large Language Models (LLMs)
We find that fine-tuning existing text embedding models on LLM-generated texts yields excellent classification accuracy.
We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
arXiv Detail & Related papers (2025-02-17T18:59:02Z) - 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) - A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution [57.309390098903]
Authorship attribution aims to identify the origin or author of a document.
Large Language Models (LLMs) with their deep reasoning capabilities and ability to maintain long-range textual associations offer a promising alternative.
Our results on the IMDb and blog datasets show an impressive 85% accuracy in one-shot authorship classification across ten authors.
arXiv Detail & Related papers (2024-10-29T04:14:23Z) - Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
"Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.
We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.
We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
arXiv Detail & Related papers (2024-10-24T17:56:08Z) - Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering [9.86691461253151]
We introduce a novel method for attribution in contextual question answering, leveraging the hidden state representations of large language models (LLMs)
Our approach bypasses the need for extensive model retraining and retrieval model overhead, offering granular attributions and preserving the quality of generated answers.
We present Verifiability-granular, an attribution dataset which has token level annotations for LLM generations in the contextual question answering setup.
arXiv Detail & Related papers (2024-05-28T09:12:44Z) - ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
Large language models (LLMs) generate human-preferable texts.
In this paper, we identify the common characteristics shared by these models.
We propose two training schemes to further improve the detection ability of the reward model.
arXiv Detail & Related papers (2024-05-27T17:38:33Z) - Calibrating Large Language Models Using Their Generations Only [44.26441565763495]
APRICOT is a method to set confidence targets and train an additional model that predicts an LLM's confidence based on its textual input and output alone.
It is conceptually simple, does not require access to the target model beyond its output, does not interfere with the language generation, and has a multitude of potential usages.
We show how our approach performs competitively in terms of calibration error for white-box and black-box LLMs on closed-book question-answering to detect incorrect LLM answers.
arXiv Detail & Related papers (2024-03-09T17:46:24Z) - LLM-Detector: Improving AI-Generated Chinese Text Detection with
Open-Source LLM Instruction Tuning [4.328134379418151]
Existing AI-generated text detection models are prone to in-domain over-fitting.
We propose LLM-Detector, a novel method for both document-level and sentence-level text detection.
arXiv Detail & Related papers (2024-02-02T05:54:12Z) - LLMDet: A Third Party Large Language Models Generated Text Detection
Tool [119.0952092533317]
Large language models (LLMs) are remarkably close to high-quality human-authored text.
Existing detection tools can only differentiate between machine-generated and human-authored text.
We propose LLMDet, a model-specific, secure, efficient, and extendable detection tool.
arXiv Detail & Related papers (2023-05-24T10:45:16Z)
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.