Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
- URL: http://arxiv.org/abs/2305.16617v3
- Date: Tue, 4 Jun 2024 07:05:48 GMT
- Title: Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model
- Authors: Yibo Miao, Hongcheng Gao, Hao Zhang, Zhijie Deng,
- Abstract summary: We propose a new method to detect machine-generated text, especially from large language models (LLMs)
We use a Bayesian surrogate model, which allows us to select typical samples based on Bayesian uncertainty and interpolate scores from typical samples to other samples, to improve query efficiency.
Empirical results demonstrate that our method significantly outperforms existing approaches under a low query budget.
- Score: 14.98695074168234
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The detection of machine-generated text, especially from large language models (LLMs), is crucial in preventing serious social problems resulting from their misuse. Some methods train dedicated detectors on specific datasets but fall short in generalizing to unseen test data, while other zero-shot ones often yield suboptimal performance. Although the recent DetectGPT has shown promising detection performance, it suffers from significant inefficiency issues, as detecting a single candidate requires querying the source LLM with hundreds of its perturbations. This paper aims to bridge this gap. Concretely, we propose to incorporate a Bayesian surrogate model, which allows us to select typical samples based on Bayesian uncertainty and interpolate scores from typical samples to other samples, to improve query efficiency. Empirical results demonstrate that our method significantly outperforms existing approaches under a low query budget. Notably, when detecting the text generated by LLaMA family models, our method with just 2 or 3 queries can outperform DetectGPT with 200 queries.
Related papers
- Dense Object Detection Based on De-homogenized Queries [12.33849715319161]
Dense object detection is widely used in automatic driving, video surveillance, and other fields.
Currently, detection methods based on greedy algorithms, such as non-maximum suppression (NMS), often produce many repetitive predictions or missed detections in dense scenarios.
Through the end-to-end DETR (DEtection TRansformer), as a type of detector that can incorporate the post-processing de-duplication capability of NMS, etc., into the network, we found that homogeneous queries in the query-based detector lead to a reduction in the de-duplication capability of the network and the learning efficiency of the encoder
arXiv Detail & Related papers (2025-02-11T02:36:10Z) - Scaling Flaws of Verifier-Guided Search in Mathematical Reasoning [16.824343439487617]
Large language models (LLMs) struggle with multi-step reasoning, where inference-time scaling has emerged as a promising strategy for performance improvement.
Verifier-guided search outperforms repeated sampling when sample size is limited by selecting and prioritizing valid reasoning paths.
As sample size increases, verifier-guided search exhibits diminishing advantages and eventually underperforms repeated sampling.
arXiv Detail & Related papers (2025-02-01T02:08:49Z) - Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Method [108.56493934296687]
We introduce a divergence-based calibration method, inspired by the divergence-from-randomness concept, to calibrate token probabilities for pretraining data detection.
We have developed a Chinese-language benchmark, PatentMIA, to assess the performance of detection approaches for LLMs on Chinese text.
arXiv Detail & Related papers (2024-09-23T07:55:35Z) - On Speeding Up Language Model Evaluation [48.51924035873411]
Development of prompt-based methods with Large Language Models (LLMs) requires making numerous decisions.
We propose a novel method to address this challenge.
We show that it can identify the top-performing method using only 5-15% of the typically needed resources.
arXiv Detail & Related papers (2024-07-08T17:48:42Z) - LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Most LLMs struggle on SummEdits, with performance close to random chance.
The best-performing model, GPT-4, is still 8% below estimated human performance.
arXiv Detail & Related papers (2023-05-23T21:50:06Z) - MGTBench: Benchmarking Machine-Generated Text Detection [54.81446366272403]
This paper proposes the first benchmark framework for MGT detection against powerful large language models (LLMs)
We show that a larger number of words in general leads to better performance and most detection methods can achieve similar performance with much fewer training samples.
Our findings indicate that the model-based detection methods still perform well in the text attribution task.
arXiv Detail & Related papers (2023-03-26T21:12:36Z) - DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability
Curvature [143.5381108333212]
We show that text sampled from an large language model tends to occupy negative curvature regions of the model's log probability function.
We then define a new curvature-based criterion for judging if a passage is generated from a given LLM.
We find DetectGPT is more discriminative than existing zero-shot methods for model sample detection.
arXiv Detail & Related papers (2023-01-26T18:44:06Z) - Unsupervised Model Selection for Time-series Anomaly Detection [7.8027110514393785]
We identify three classes of surrogate (unsupervised) metrics, namely, prediction error, model centrality, and performance on injected synthetic anomalies.
We formulate metric combination with multiple imperfect surrogate metrics as a robust rank aggregation problem.
Large-scale experiments on multiple real-world datasets demonstrate that our proposed unsupervised approach is as effective as selecting the most accurate model.
arXiv Detail & Related papers (2022-10-03T16:49:30Z) - Pareto Optimization for Active Learning under Out-of-Distribution Data
Scenarios [79.02009938011447]
We propose a sampling scheme, which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool.
Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
arXiv Detail & Related papers (2022-07-04T04:11:44Z)
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.