In enterprise applications, selecting an appropriate language model (LLM) is crucial. However, current model evaluation methods, such as scoring and ranking, are often troubled by data contamination issues, resulting in discrepancies between the model's performance in practical applications and evaluation results. This article explores data contamination issues in model evaluation and, in conjunction with the HaxiTAG team's understanding, endorses and improves upon the LLM Decontaminator proposed by LMSYS to enhance evaluation accuracy and reliability.
Challenges with Public Test Datasets
Public test datasets and general capability test datasets are widely used in the development and algorithm design of LLMs. However, these datasets face contamination risks, where information from the test set leaks into the training set, leading to overly optimistic performance estimates. Despite common detection methods such as n-gram overlap and embedding similarity search, they struggle to address the challenge of rewritten samples.
For example, in benchmark tests like HumanEval and GSM-8K, we observed that using rewriting techniques can enable a 13B model to achieve a high score of 85.9 in the MMLU benchmark, yet existing detection methods (such as n-gram overlap and embedding similarity) fail to detect this contamination. This indicates that solely relying on current methods cannot accurately assess the model's actual performance.
The Introduction of the LLM Decontaminator
To address these issues, the HaxiTAG team has proposed an improved contamination detection method—the LLM Decontaminator. This method consists of two steps:
- Embedding Similarity Search: Using embedding similarity search to identify the top k training items with the highest similarity.
- Generation and Evaluation of Rewriting Pairs: Generating k potential rewriting pairs from these items and using advanced LLMs to rephrase and evaluate each pair.
In our experiments, the LLM Decontaminator significantly outperformed existing methods in removing rewritten samples. For instance, in the MMLU benchmark test, the LLM Decontaminator achieved an F1 score of 0.92 in detecting 200 prompt pairs, whereas the F1 scores for n-gram overlap and embedding similarity methods were 0.73 and 0.68, respectively.
Evaluation and Comparison
To comprehensively assess the effectiveness of different detection methods, we constructed 200 prompt pairs in the MMLU benchmark test, including 100 random pairs and 100 rewritten pairs. The results showed that the LLM Decontaminator achieved the highest F1 score in all cases, indicating its robustness in detecting contamination. Additionally, we applied the LLM Decontaminator to real-world datasets, such as Stack and RedPajama, identifying a large number of rewritten samples.
In these datasets, the CodeAlpaca dataset, which contains 20K instruction-following synthetic data, had a contamination ratio of 12.3% detected by the LLM Decontaminator. The contamination ratio between training and test splits in the MATH benchmark's math problems was 8.7%. In the StarCoder-Data programming dataset, despite initial decontamination processing, 5.4% of samples were detected as rewritten by the LLM Decontaminator.
HaxiTAG Team's Insights and Recommendations
In model performance testing, the HaxiTAG team, based on enterprise scenarios and needs, conducts specific capability, model test dataset tests, and constructs specialized datasets to perform capability, performance, and optimization goal preventative testing. We recognize that avoiding biases caused by data contamination is crucial in the actual business operation and application of models.
The HaxiTAG team recommends adopting stronger decontamination methods when using any public benchmarks. Our proposed LLM Decontaminator is open-sourced on GitHub for community use. Through the following steps, enterprises can preprocess training and test data to ensure more accurate model evaluations:
- Data Preprocessing: The LLM Decontaminator accepts jsonl formatted datasets, where each line corresponds to an {"text": data} entry.
- End-to-End Detection: Construct a top-k similarity database using Sentence BERT and use GPT-4 to check each item for rewrites individually.
Conclusion
Data contamination is a key issue affecting the accuracy of LLM model evaluations. By proposing the LLM Decontaminator, the HaxiTAG team has revealed significant contamination phenomena in existing datasets and calls for the community to reconsider benchmarks and decontamination methods in the context of LLMs. We recommend using more robust decontamination tools when evaluating LLMs on public benchmarks to enhance evaluation accuracy and reliability.
We hope that enterprises, when selecting and evaluating LLM models, are aware of the potential risks of data contamination and take effective decontamination measures to ensure that the models have stable and reliable performance in practical applications.
TAGS
LLM model selection for enterprises, LLM decontamination strategies, HaxiTAG team's insights on LLM, data contamination in LLM evaluation, embedding similarity search for LLM, MMLU benchmark test results, improving LLM evaluation accuracy, LLM decontaminator method, public test dataset contamination, avoiding biases in LLM models
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