Data Contamination Can Cross Language Barriers
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address the issue of data contamination in large language models (LLMs), specifically focusing on cross-lingual contamination that can deceive existing detection methods . This problem is not entirely new, but the paper introduces a novel approach to uncover deeply concealed contamination that can inflate LLMs' performance without being detected by current methods . The research highlights the importance of detecting and mitigating such contamination to ensure the reliability and integrity of LLMs' performance evaluations .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the scientific hypothesis that the knowledge in a model can be fixed, and language acts as an interface, affecting the performance of large language models (LLMs) across different languages . The study explores how the same backbone model's performance can vary significantly when pre-trained on the same benchmark data in different languages, suggesting that language quality acts as an interface influencing the model's ability to understand and generate text .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "Data Contamination Can Cross Language Barriers" introduces innovative ideas, methods, and models related to detecting and addressing contamination in large language models (LLMs) . The paper presents a novel form of contamination called cross-lingual contamination, which artificially inflates LLMs' performance by overfitting them on translated versions of benchmark test sets, evading traditional detection methods . To address this, the paper proposes generalization-based approaches that involve modifying the original benchmark by replacing false answer choices with correct ones from other questions to unmask deeply concealed contamination . This method, known as "choice confusion," aims to evaluate the model's ability to generalize to easier situations where false choices are replaced with correct ones, revealing contamination that affects the model's performance .
Furthermore, the paper discusses the construction of a generalized benchmark to test the model's performance on an easier variant of the benchmark, highlighting the impact of contamination on model performance . By replacing false choices with correct ones from other questions and shuffling them, the paper aims to assess the model's ability to excel when faced with simplified scenarios, indicating potential contamination if the model struggles to adapt . This method provides insights into the model's understanding and memorization capabilities, especially in detecting non-generalizable knowledge injected during pre-training .
Moreover, the paper evaluates contaminated models on original English benchmarks to assess their impact on leaderboard rankings . By injecting contamination in non-English languages and evaluating them on English benchmarks, the paper reveals how contamination can mislead the leaderboard rankings . The evaluation includes clean models, vanilla contaminated models, and cross-lingual contaminated models, showing significant performance inflation in cross-lingual contamination scenarios, even across language barriers . This analysis sheds light on the potential risks of contamination in LLMs and its implications for model performance and evaluation . The paper "Data Contamination Can Cross Language Barriers" introduces a novel generalization-based approach to detect contamination in large language models (LLMs) that surpasses the limitations of traditional memorization-based methods . This method involves constructing a generalized benchmark by replacing false choices with correct ones from other questions and shuffling them, ensuring that models cannot rely on answer order shortcuts . By evaluating the model's performance on this modified benchmark, the proposed method can effectively detect contamination that affects the model's ability to generalize and understand the underlying concepts .
Compared to previous memorization-based methods that focus on n-gram duplication between pre-training and evaluation data, the generalization-based approach in the paper provides a more comprehensive definition of contamination . It goes beyond detecting specific text memorization to identify non-generalizable knowledge acquired by the model, such as contamination from translated or paraphrased forms of benchmarks . This broader definition allows the method to uncover deep forms of contamination, like cross-lingual contamination, that elude traditional detection techniques .
One key advantage of the generalization-based approach is its ability to assess model performance on a modified benchmark that tests the model's adaptability to easier scenarios created by replacing false choices with correct ones from other questions . This method, known as "choice confusion," can reveal contamination that hampers the model's performance by causing confusion when faced with simplified questions . By measuring the difference in model performance between the original and generalized benchmarks, the proposed method effectively identifies potential contamination that impacts the model's understanding and generalization capabilities .
Furthermore, the paper highlights the limitations of existing memorization-based methods, such as shared likelihood and guided prompting, in detecting deep forms of contamination like cross-lingual contamination . These methods struggle to differentiate between contaminated and clean models, especially when faced with subtle contamination that affects model performance across languages . In contrast, the generalization-based approach offers a more robust and versatile detection mechanism that can uncover contamination beyond specific text memorization, providing a comprehensive solution to address contamination in LLMs .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research studies have been conducted in the field of data contamination in language models. Noteworthy researchers in this area include Feng Yao, Yufan Zhuang, Zihao Sun, Sunan Xu, Animesh Kumar, and Jingbo Shang from the University of California, San Diego . Other prominent researchers mentioned in the context are Isaac Cowhey Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord . Additionally, Chunyuan Deng, Yilun Zhao, Xiangru Tang, Mark Gerstein, and Arman Cohan have also contributed to investigating data contamination in modern benchmarks for large language models .
The key to the solution mentioned in the paper involves proposing generalization-based approaches to unmask deeply concealed contamination in large language models (LLMs). Specifically, the proposed method involves modifying the original benchmark by replacing false answer choices with correct ones from other questions to create a generalized version of the benchmark. By examining the LLM's performance change in these modified scenarios, contaminated models that struggle to generalize to easier situations can be identified, where all choices are correct, leading to the detection of contamination that evades current detection methods .
How were the experiments in the paper designed?
The experiments in the paper "Data Contamination Can Cross Language Barriers" were designed to investigate the feasibility of deep forms of contamination, determine the ability of existing methods to detect such contamination, and propose detection methods capable of identifying deeply concealed contamination . The experiments involved intentionally injecting cross-lingual contamination into open-sourced models to create contaminated models for evaluation . To conduct the experiments, two multilingual Large Language Models (LLMs), LLaMA3-8B and Qwen1.5-7B, were used as backbone models . The contamination injection was done separately for different benchmarks, ensuring that each model only contained contamination from one specific benchmark in a single language . The contaminated models were then evaluated on the original English benchmarks to assess their impact on misleading the leaderboard . The evaluation included comparing the performance of clean models, vanilla contaminated models, and cross-lingual contaminated models . The experiments aimed to demonstrate how cross-lingual contamination can inflate LLMs' performance and how it can be detected using generalization-based detection methods .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is MMLU, ARC-Challenge, and MathQA . The code for these evaluations is open source and can be accessed through various repositories such as EleutherAI, Hugging Face, and Microsoft .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study focused on investigating cross-lingual contamination in large language models (LLMs) and proposed methods to detect and address this type of contamination . The experiments conducted aimed to verify the feasibility of deep forms of contamination, determine the effectiveness of existing detection methods, and propose new detection approaches capable of identifying deeply concealed contamination . The results demonstrated that cross-lingual contamination can indeed deceive existing detection methods but not the newly proposed methods, highlighting the importance of addressing this issue .
Furthermore, the study acknowledged its limitations, such as the focus on 7B LLMs for contamination injection and the restriction to multiple-choice question-answering benchmarks, which may limit the generalizability of the findings . Despite these limitations, the experiments conducted provided valuable insights into the potential impact of cross-lingual contamination on LLMs' performance and the need for stronger detection methods to uncover such contamination .
Overall, the experiments and results in the paper offer robust support for the scientific hypotheses under investigation, showcasing the significance of addressing cross-lingual contamination in large language models and the potential implications for model performance and reliability .
What are the contributions of this paper?
The paper makes several key contributions:
- It introduces a cross-lingual form of contamination in large language models (LLMs) that can inflate performance while evading current detection methods .
- The paper proposes generalization-based approaches to unmask deeply concealed contamination by modifying benchmark test sets and examining the LLM's performance changes .
- It discusses the potential utilization of cross-lingual contamination in interpreting LLMs' working mechanisms and enhancing their multilingual capabilities .
- The investigation aims to verify deep forms of contamination, determine the effectiveness of existing contamination detection methods, and propose new detection methods capable of identifying deeply concealed contamination .
- The study explores how LLMs think across languages, highlighting the significant performance variations of the same backbone model when pre-trained on benchmark data in different languages .
What work can be continued in depth?
Further work in this area can focus on addressing the limitations identified in the investigation. One aspect to explore is whether cross-lingual contamination behaves consistently across different sizes of LLMs beyond the 7B models used in the study . Additionally, expanding the detection of contamination beyond multiple-choice question-answering benchmarks to other types of benchmarks can provide a more comprehensive understanding of contamination detection . Furthermore, investigating the injection of contamination across multiple benchmarks and languages simultaneously, rather than separately, would better reflect real-world scenarios where various benchmarks and languages are intertwined . These avenues of research can enhance the detection methods to uncover potential undisclosed contamination more effectively.