A study by UC Berkeley and Shanghai Jiao Tong University highlights the challenges in evaluating language models due to contaminated datasets. Conventional decontamination techniques are flawed, prompting the researchers to propose a new approach using rephrased samples and embedding similarity search. The study emphasizes the need for more thorough decontamination procedures and suggests new tests for fair evaluation of language models.
**Researchers Introduce the Concept of a ‘Rephrased Sample’ to Address Issues with Language Models**
Researchers from UC Berkeley and Shanghai Jiao Tong University have identified a significant issue with language models, such as GPT-4, PaLM, and Llama. They have found that popular benchmarks used to evaluate language models may have tainted datasets, leading to inaccurate performance measurement.
To detect contamination in these models, traditional methods like n-gram overlap and embedding similarity search are utilized. However, these methods have limitations in terms of precision and recall. Moreover, the use of synthetic data, generated by GPT-4 and other large language models (LLMs), adds complexity to the contamination detection process.
The researchers propose a new approach called the “rephrased sample.” Rephrased samples have the same meaning as the original samples but are difficult to identify using existing contamination tests. The researchers demonstrate that training models using these rephrased samples can lead to overfitting and unrealistically high performance on benchmarks. They also reveal that even a finely calibrated Llama model can achieve similar performance to GPT-4 without being detected by n-gram overlap contamination tests.
To address these issues, the researchers suggest an LLM-based decontamination technique. This method involves using an embedding similarity search to identify models that are too similar to the test instance. The researchers demonstrate the effectiveness of their approach compared to conventional techniques. Additionally, they uncover a sizable amount of rephrased samples in GPT-3.5’s synthetic dataset, suggesting potential contamination during training with LLM-generated fake data.
The researchers call for the establishment of more rigorous decontamination procedures for evaluating LLMs using public benchmarks. They propose the creation of new, one-time tests, such as Codeforces and Kaggle competitions, to ensure fair evaluation and overcome these fundamental issues.
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