Assessing Contamination in Large Language Models: Introducing the LogProber method
- URL: http://arxiv.org/abs/2408.14352v1
- Date: Mon, 26 Aug 2024 15:29:34 GMT
- Title: Assessing Contamination in Large Language Models: Introducing the LogProber method
- Authors: Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri,
- Abstract summary: In machine learning, contamination refers to situations where testing data leak into the training set.
In the present paper we introduce LogProber, a novel, efficient, algorithm that we show able to detect contamination using token probability in given sentences.
- Score: 17.91379291654773
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In machine learning, contamination refers to situations where testing data leak into the training set. The issue is particularly relevant for the evaluation of the performance of Large Language Models (LLMs), which are generally trained on gargantuan, and generally opaque, corpora of text scraped from the world wide web. Developing tools to detect contamination is therefore crucial to be able to fairly and properly track the evolution of the performance of LLMs. Most recent works in the field are not tailored to quantify contamination on short sequences of text like we find in psychology questionnaires. In the present paper we introduce LogProber, a novel, efficient, algorithm that we show able to detect contamination using token probability in given sentences. In the second part we investigate the limitations of the method and discuss how different training methods can contaminate models without leaving traces in the token probabilities.
Related papers
- LLM Performance for Code Generation on Noisy Tasks [0.41942958779358674]
We show that large language models (LLMs) can solve tasks obfuscated to a level where the text would be unintelligible to human readers.<n>We report empirical evidence of distinct performance decay patterns between contaminated and unseen datasets.<n>We propose measuring the decay of performance under obfuscation as a possible strategy for detecting dataset contamination.
arXiv Detail & Related papers (2025-05-29T16:11:18Z) - GaussMark: A Practical Approach for Structural Watermarking of Language Models [61.84270985214254]
GaussMark is a simple, efficient, and relatively robust scheme for watermarking large language models.
We show that GaussMark is reliable, efficient, and relatively robust to corruptions such as insertions, deletions, substitutions, and roundtrip translations.
arXiv Detail & Related papers (2025-01-17T22:30:08Z) - CAP: Data Contamination Detection via Consistency Amplification [20.135264289668463]
Large language models (LLMs) are widely used, but concerns about data contamination challenge their reliability.
We propose a novel framework, Consistency Amplification-based Data Contamination Detection (CAP), which introduces the Performance Consistency Ratio (PCR) to measure dataset leakage.
CAP is applicable to various benchmarks and works for both white-box and black-box models.
arXiv Detail & Related papers (2024-10-19T06:33:33Z) - 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) - Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges [3.0455427910850785]
We evaluate five contamination detection approaches with four state-of-the-art LLMs across eight challenging datasets.<n>Our analysis reveals that current methods have non-trivial limitations in their assumptions and practical applications.
arXiv Detail & Related papers (2024-09-16T02:04:33Z) - Adaptive Pre-training Data Detection for Large Language Models via Surprising Tokens [1.2549198550400134]
Large language models (LLMs) are extensively used, but there are concerns regarding privacy, security, and copyright due to their opaque training data.
Current solutions to this problem leverage techniques explored in machine learning privacy such as Membership Inference Attacks (MIAs)
We propose an adaptive pre-training data detection method which alleviates this reliance and effectively amplify the identification.
arXiv Detail & Related papers (2024-07-30T23:43:59Z) - Data Contamination Can Cross Language Barriers [29.103517721155487]
The opacity in developing large language models (LLMs) is raising growing concerns about the potential contamination of public benchmarks in the pre-training data.
We first present a cross-lingual form of contamination that inflates LLMs' performance while evading current detection methods.
We propose generalization-based approaches to unmask such deeply concealed contamination.
arXiv Detail & Related papers (2024-06-19T05:53:27Z) - A Comprehensive Survey of Contamination Detection Methods in Large Language Models [68.10605098856087]
With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges.
LLMs' performance may not be reliable anymore, as the high performance may be at least partly due to their previous exposure to the data.
This limitation jeopardizes real capability improvement in the field of NLP, yet there remains a lack of methods on how to efficiently detect contamination.
arXiv Detail & Related papers (2024-03-31T14:32:02Z) - Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flows [53.31856123113228]
This paper proposes Language Rectified Flow (ours)
Our method is based on the reformulation of the standard probabilistic flow models.
Experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many NLP tasks.
arXiv Detail & Related papers (2024-03-25T17:58:22Z) - Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification [116.77055746066375]
Large language models (LLMs) are notorious for hallucinating, i.e., producing erroneous claims in their output.
We propose a novel fact-checking and hallucination detection pipeline based on token-level uncertainty quantification.
arXiv Detail & Related papers (2024-03-07T17:44:17Z) - KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models [53.84677081899392]
KIEval is a Knowledge-grounded Interactive Evaluation framework for large language models.
It incorporates an LLM-powered "interactor" role for the first time to accomplish a dynamic contamination-resilient evaluation.
Extensive experiments on seven leading LLMs across five datasets validate KIEval's effectiveness and generalization.
arXiv Detail & Related papers (2024-02-23T01:30:39Z) - Investigating Data Contamination for Pre-training Language Models [46.335755305642564]
We explore the impact of data contamination at the pre-training stage by pre-training a series of GPT-2 models.
We highlight the effect of both text contamination (textiti.e. input text of the evaluation samples) and ground-truth contamination (textiti.e. the prompts asked on the input and the desired outputs) from evaluation data.
arXiv Detail & Related papers (2024-01-11T17:24:49Z) - Token-Level Adversarial Prompt Detection Based on Perplexity Measures
and Contextual Information [67.78183175605761]
Large Language Models are susceptible to adversarial prompt attacks.
This vulnerability underscores a significant concern regarding the robustness and reliability of LLMs.
We introduce a novel approach to detecting adversarial prompts at a token level.
arXiv Detail & Related papers (2023-11-20T03:17:21Z) - Understanding and Mitigating Classification Errors Through Interpretable
Token Patterns [58.91023283103762]
Characterizing errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors.
We propose to discover those patterns of tokens that distinguish correct and erroneous predictions.
We show that our method, Premise, performs well in practice.
arXiv Detail & Related papers (2023-11-18T00:24:26Z) - Detecting Pretraining Data from Large Language Models [90.12037980837738]
We study the pretraining data detection problem.
Given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text?
We introduce a new detection method Min-K% Prob based on a simple hypothesis.
arXiv Detail & Related papers (2023-10-25T17:21:23Z) - A New Benchmark and Reverse Validation Method for Passage-level
Hallucination Detection [63.56136319976554]
Large Language Models (LLMs) generate hallucinations, which can cause significant damage when deployed for mission-critical tasks.
We propose a self-check approach based on reverse validation to detect factual errors automatically in a zero-resource fashion.
We empirically evaluate our method and existing zero-resource detection methods on two datasets.
arXiv Detail & Related papers (2023-10-10T10:14:59Z) - 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) - Sample Efficient Approaches for Idiomaticity Detection [6.481818246474555]
This work explores sample efficient methods of idiomaticity detection.
In particular, we study the impact of Pattern Exploit Training (PET), a few-shot method of classification, and BERTRAM, an efficient method of creating contextual embeddings.
Our experiments show that whilePET improves performance on English, they are much less effective on Portuguese and Galician, leading to an overall performance about on par with vanilla mBERT.
arXiv Detail & Related papers (2022-05-23T13:46:35Z)
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.