Researchers from UC Berkeley and SJTU China Introduce the Concept of a ‘Rephrased Sample’ for Rethinking Benchmark and Contamination for Language Models

Giant language fashions have gotten more and more complicated, making analysis harder. The neighborhood has produced many benchmarks in a comparatively quick period of time, however benchmark scores don’t all the time correspond to precise efficiency. Some proof means that many common benchmarks could have tainted datasets used for fine-tuning and pre-training.

Regardless of widespread settlement that it’s an necessary challenge, pinpointing the supply of air pollution has been troublesome. Each n-gram overlap and embedding similarity search are broadly employed. String matching is used extensively by state-of-the-art improvements like GPT-4, PaLM, and Llama for N-gram overlap contamination detection. Nonetheless, its precision is considerably low. An embedding similarity search seems on the embeddings of beforehand skilled fashions (like BERT) to find associated and possibly polluted circumstances. Nonetheless, discovering the candy spot between recall and precision when deciding on a similarity stage is perhaps troublesome. As well as, there’s a creating pattern in mannequin coaching that makes use of artificial knowledge generated by LLMs (e.g., GPT-4), the place contamination could also be much more troublesome to establish utilizing string matching.

To look at decontamination strategies, a brand new research by UC Berkeley and Shanghai Jiao Tong College introduces the idea of a “rephrased pattern,” which has the identical semantics as the unique pattern however is tough to establish by current contamination assessments. LLMs generate rephrased samples by translating and paraphrasing take a look at samples into one other language. The researchers show that if such paraphrased examples are utilized for coaching, the ensuing mannequin is very inclined to overfitting and may obtain extraordinarily excessive efficiency on take a look at benchmarks. A finely calibrated 13B Llama mannequin can match GPT -4’s efficiency throughout all benchmarks whereas remaining unnoticed by n-gram overlap as contamination. This habits is noticed in broadly used benchmarks like MMLU, GSM-8k, and HumanEval. Consequently, the flexibility to establish rephrased samples is essential. 

The researchers clarify the issues in standard decontamination strategies and counsel a novel LLM-based method. To find out if any top-k samples are too just like the take a look at occasion, they first apply an embedding similarity search to seek out probably the most comparable fashions to the take a look at pattern in query. The outcomes show the prevalence of their advised LLM decontaminator over standard strategies. They take a look at their decontaminator on quite a lot of common datasets which can be used for fine-tuning and preliminary coaching. It’s additionally discovered that GPT-3.5’s artificial dataset, CodeAlpaca, has a large quantity of rephrased samples from HumanEval (12.8% to be precise). This hints at a possible for contamination throughout coaching with LLM-created pretend knowledge. 

The researchers advise the neighborhood to ascertain extra thorough decontamination procedures for evaluating LLMs utilizing public benchmarks. They hope to create new, one-time assessments, like Codeforces and Kaggle competitions, for the truthful analysis of LLMs to beat these basic points.

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