Summary of Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-source Llms, by Simone Balloccu et al.
Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs
by Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, Ondřej Dušek
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the problem of data contamination in Natural Language Processing (NLP) research, specifically focusing on Large Language Models (LLMs). The authors investigate OpenAI’s GPT-3.5 and GPT-4 models, which are widely used but lack transparency regarding training data. By analyzing 255 papers and OpenAI’s data usage policy, the study reveals that these models have been exposed to approximately 4.7 million samples from 263 benchmarks over a one-year period. The paper also highlights evaluation malpractices in reviewed papers, such as unfair comparisons and reproducibility issues. This research aims to provide a systematic understanding of data contamination and promote best practices in LLM usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models are being used in research. It’s important because some people worry that these models might be learning from private data, which could be bad. The researchers looked at two very popular models, GPT-3.5 and GPT-4, and found out how much data they’ve been exposed to over the past year. They also saw some problems with how other researchers are testing these models. This study wants to help make sure that big language models are used safely and fairly. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Nlp
